id	sid	tid	token	lemma	pos
hjic-1020	1	1	hungarian	hungarian	ADJ
hjic-1020	1	2	journal	journal	NOUN
hjic-1020	1	3	of	of	ADP
hjic-1020	1	4	industrial	industrial	ADJ
hjic-1020	1	5	chemistry	chemistry	NOUN
hjic-1020	1	6	veszprem	veszprem	PROPN
hjic-1020	1	7	vol	vol	NOUN
hjic-1020	1	8	.	.	PROPN
hjic-1020	2	1	30	30	NUM
hjic-1020	2	2	.	.	PUNCT
hjic-1020	3	1	pp	pp	ADJ
hjic-1020	3	2	.	.	PUNCT
hjic-1020	4	1	241	241	NUM
hjic-1020	4	2	-	-	SYM
hjic-1020	4	3	245	245	NUM
hjic-1020	4	4	(	(	PUNCT
hjic-1020	4	5	2002	2002	NUM
hjic-1020	4	6	)	)	PUNCT
hjic-1020	4	7	classification	classification	NOUN
hjic-1020	4	8	neural	neural	ADJ
hjic-1020	4	9	networks	network	NOUN
hjic-1020	4	10	improve	improve	VERB
hjic-1020	4	11	:	:	PUNCT
hjic-1020	4	12	ment	ment	NOUN
hjic-1020	4	13	using	use	VERB
hjic-1020	4	14	genetic	genetic	ADJ
hjic-1020	4	15	algorithms	algorithm	NOUN
hjic-1020	4	16	a.	a.	NOUN
hjic-1020	4	17	woinaroschy	woinaroschy	PROPN
hjic-1020	4	18	,	,	PUNCT
hjic-1020	4	19	v.	v.	ADP
hjic-1020	4	20	plesuand	plesuand	PROPN
hjic-1020	4	21	k.	k.	PROPN
hjic-1020	4	22	woinaroschy	woinaroschy	PROPN
hjic-1020	5	1	(	(	PUNCT
hjic-1020	5	2	department	department	NOUN
hjic-1020	5	3	of	of	ADP
hjic-1020	5	4	chemical	chemical	PROPN
hjic-1020	5	5	engineering	engineering	NOUN
hjic-1020	5	6	,	,	PUNCT
hjic-1020	5	7	university	university	NOUN
hjic-1020	5	8	politehnica	politehnica	NOUN
hjic-1020	5	9	of	of	ADP
hjic-1020	5	10	bucharest	buchar	ADJ
hjic-1020	5	11	,	,	PUNCT
hjic-1020	5	12	1	1	NUM
hjic-1020	5	13	-	-	SYM
hjic-1020	5	14	5	5	NUM
hjic-1020	5	15	,	,	PUNCT
hjic-1020	5	16	polizu	polizu	PROPN
hjic-1020	5	17	street	street	PROPN
hjic-1020	5	18	,	,	PUNCT
hjic-1020	5	19	bucharest	buchar	ADJ
hjic-1020	5	20	78126	78126	NUM
hjic-1020	5	21	,	,	PUNCT
hjic-1020	5	22	romania	romania	PROPN
hjic-1020	5	23	)	)	PUNCT
hjic-1020	5	24	received	receive	VERB
hjic-1020	5	25	:	:	PUNCT
hjic-1020	5	26	april	april	PROPN
hjic-1020	5	27	8	8	NUM
hjic-1020	5	28	,	,	PUNCT
hjic-1020	5	29	2002	2002	NUM
hjic-1020	5	30	the	the	DET
hjic-1020	5	31	aim	aim	NOUN
hjic-1020	5	32	of	of	ADP
hjic-1020	5	33	the	the	DET
hjic-1020	5	34	present	present	ADJ
hjic-1020	5	35	work	work	NOUN
hjic-1020	5	36	consists	consist	VERB
hjic-1020	5	37	the	the	DET
hjic-1020	5	38	development	development	NOUN
hjic-1020	5	39	of	of	ADP
hjic-1020	5	40	a	a	DET
hjic-1020	5	41	procedure	procedure	NOUN
hjic-1020	5	42	capable	capable	ADJ
hjic-1020	5	43	to	to	PART
hjic-1020	5	44	realize	realize	VERB
hjic-1020	5	45	a	a	DET
hjic-1020	5	46	high	high	ADJ
hjic-1020	5	47	accurate	accurate	ADJ
hjic-1020	5	48	classification	classification	NOUN
hjic-1020	5	49	neural	neural	ADJ
hjic-1020	5	50	network	network	NOUN
hjic-1020	5	51	with	with	ADP
hjic-1020	5	52	a	a	DET
hjic-1020	5	53	reduced	reduce	VERB
hjic-1020	5	54	number	number	NOUN
hjic-1020	5	55	of	of	ADP
hjic-1020	5	56	neurons	neuron	NOUN
hjic-1020	5	57	.	.	PUNCT
hjic-1020	6	1	in	in	ADP
hjic-1020	6	2	order	order	NOUN
hjic-1020	6	3	to	to	PART
hjic-1020	6	4	avoid	avoid	VERB
hjic-1020	6	5	local	local	ADJ
hjic-1020	6	6	minima	minima	NOUN
hjic-1020	6	7	.	.	PUNCT
hjic-1020	7	1	the	the	DET
hjic-1020	7	2	weights	weight	NOUN
hjic-1020	7	3	of	of	ADP
hjic-1020	7	4	a	a	DET
hjic-1020	7	5	proposed	propose	VERB
hjic-1020	7	6	three	three	NUM
hjic-1020	7	7	-	-	PUNCT
hjic-1020	7	8	layer	layer	NOUN
hjic-1020	7	9	network	network	NOUN
hjic-1020	7	10	were	be	AUX
hjic-1020	7	11	computed	compute	VERB
hjic-1020	7	12	using	use	VERB
hjic-1020	7	13	a	a	DET
hjic-1020	7	14	common	common	ADJ
hjic-1020	7	15	genetic	genetic	ADJ
hjic-1020	7	16	algorithm	algorithm	NOUN
hjic-1020	7	17	.	.	PUNCT
hjic-1020	8	1	the	the	DET
hjic-1020	8	2	performances	performance	NOUN
hjic-1020	8	3	of	of	ADP
hjic-1020	8	4	the	the	DET
hjic-1020	8	5	genetic	genetic	ADJ
hjic-1020	8	6	algorithm	algorithm	NOUN
hjic-1020	8	7	were	be	AUX
hjic-1020	8	8	strongly	strongly	ADV
hjic-1020	8	9	improved	improve	VERB
hjic-1020	8	10	by	by	ADP
hjic-1020	8	11	several	several	ADJ
hjic-1020	8	12	means	mean	NOUN
hjic-1020	8	13	that	that	PRON
hjic-1020	8	14	make	make	VERB
hjic-1020	8	15	an	an	DET
hjic-1020	8	16	increased	increase	VERB
hjic-1020	8	17	balance	balance	NOUN
hjic-1020	8	18	between	between	ADP
hjic-1020	8	19	exploration	exploration	NOUN
hjic-1020	8	20	and	and	CCONJ
hjic-1020	8	21	exploitation	exploitation	NOUN
hjic-1020	8	22	of	of	ADP
hjic-1020	8	23	the	the	DET
hjic-1020	8	24	search	search	NOUN
hjic-1020	8	25	space	space	NOUN
hjic-1020	8	26	.	.	PUNCT
hjic-1020	9	1	thus	thus	ADV
hjic-1020	9	2	,	,	PUNCT
hjic-1020	9	3	in	in	ADP
hjic-1020	9	4	order	order	NOUN
hjic-1020	9	5	to	to	PART
hjic-1020	9	6	decrease	decrease	VERB
hjic-1020	9	7	the	the	DET
hjic-1020	9	8	number	number	NOUN
hjic-1020	9	9	of	of	ADP
hjic-1020	9	10	genes	gene	NOUN
hjic-1020	9	11	,	,	PUNCT
hjic-1020	9	12	respectively	respectively	ADV
hjic-1020	9	13	weights	weight	NOUN
hjic-1020	9	14	,	,	PUNCT
hjic-1020	9	15	the	the	DET
hjic-1020	9	16	dimension	dimension	NOUN
hjic-1020	9	17	of	of	ADP
hjic-1020	9	18	the	the	DET
hjic-1020	9	19	output	output	NOUN
hjic-1020	9	20	vector	vector	NOUN
hjic-1020	9	21	was	be	AUX
hjic-1020	9	22	minimized	minimize	VERB
hjic-1020	9	23	by	by	ADP
hjic-1020	9	24	identification	identification	NOUN
hjic-1020	9	25	of	of	ADP
hjic-1020	9	26	classes	class	NOUN
hjic-1020	9	27	with	with	ADP
hjic-1020	9	28	binary	binary	ADJ
hjic-1020	9	29	numbers	number	NOUN
hjic-1020	9	30	.	.	PUNCT
hjic-1020	10	1	a	a	DET
hjic-1020	10	2	very	very	ADV
hjic-1020	10	3	favorable	favorable	ADJ
hjic-1020	10	4	effect	effect	NOUN
hjic-1020	10	5	was	be	AUX
hjic-1020	10	6	obtained	obtain	VERB
hjic-1020	10	7	by	by	ADP
hjic-1020	10	8	seeding	seed	VERB
hjic-1020	10	9	the	the	DET
hjic-1020	10	10	initial	initial	ADJ
hjic-1020	10	11	population	population	NOUN
hjic-1020	10	12	with	with	ADP
hjic-1020	10	13	a	a	DET
hjic-1020	10	14	good	good	ADJ
hjic-1020	10	15	chromosome	chromosome	NOUN
hjic-1020	10	16	obtained	obtain	VERB
hjic-1020	10	17	by	by	ADP
hjic-1020	10	18	the	the	DET
hjic-1020	10	19	use	use	NOUN
hjic-1020	10	20	of	of	ADP
hjic-1020	10	21	the	the	DET
hjic-1020	10	22	classical	classical	ADJ
hjic-1020	10	23	"	"	PUNCT
hjic-1020	10	24	delta	delta	NOUN
hjic-1020	10	25	-	-	PUNCT
hjic-1020	10	26	rule	rule	NOUN
hjic-1020	10	27	"	"	PUNCT
hjic-1020	10	28	learning	learn	VERB
hjic-1020	10	29	procedure	procedure	NOUN
hjic-1020	10	30	.	.	PUNCT
hjic-1020	11	1	it	it	PRON
hjic-1020	11	2	was	be	AUX
hjic-1020	11	3	also	also	ADV
hjic-1020	11	4	investigated	investigate	VERB
hjic-1020	11	5	the	the	DET
hjic-1020	11	6	effect	effect	NOUN
hjic-1020	11	7	of	of	ADP
hjic-1020	11	8	the	the	DET
hjic-1020	11	9	initial	initial	ADJ
hjic-1020	11	10	population	population	NOUN
hjic-1020	11	11	size	size	NOUN
hjic-1020	11	12	,	,	PUNCT
hjic-1020	11	13	the	the	DET
hjic-1020	11	14	bounds	bound	NOUN
hjic-1020	11	15	imposed	impose	VERB
hjic-1020	11	16	to	to	ADP
hjic-1020	11	17	the	the	DET
hjic-1020	11	18	weights	weight	NOUN
hjic-1020	11	19	of	of	ADP
hjic-1020	11	20	the	the	DET
hjic-1020	11	21	inter	inter	ADJ
hjic-1020	11	22	-	-	ADJ
hjic-1020	11	23	neuronal	neuronal	ADJ
hjic-1020	11	24	connections	connection	NOUN
hjic-1020	11	25	,	,	PUNCT
hjic-1020	11	26	the	the	DET
hjic-1020	11	27	number	number	NOUN
hjic-1020	11	28	of	of	ADP
hjic-1020	11	29	neurons	neuron	NOUN
hjic-1020	11	30	in	in	ADP
hjic-1020	11	31	the	the	DET
hjic-1020	11	32	hidden	hide	VERB
hjic-1020	11	33	layer	layer	NOUN
hjic-1020	11	34	,	,	PUNCT
hjic-1020	11	35	fitness	fitness	NOUN
hjic-1020	11	36	expression	expression	NOUN
hjic-1020	11	37	,	,	PUNCT
hjic-1020	11	38	etc	etc	X
hjic-1020	11	39	.	.	X
hjic-1020	12	1	keywords	keyword	NOUN
hjic-1020	12	2	:	:	PUNCT
hjic-1020	12	3	neural	neural	ADJ
hjic-1020	12	4	networks	network	NOUN
hjic-1020	12	5	,	,	PUNCT
hjic-1020	12	6	genetic	genetic	ADJ
hjic-1020	12	7	algorithms	algorithm	NOUN
hjic-1020	12	8	,	,	PUNCT
hjic-1020	12	9	classification	classification	NOUN
hjic-1020	12	10	introduction	introduction	NOUN
hjic-1020	12	11	an	an	DET
hjic-1020	12	12	important	important	ADJ
hjic-1020	12	13	field	field	NOUN
hjic-1020	12	14	of	of	ADP
hjic-1020	12	15	neural	neural	ADJ
hjic-1020	12	16	network	network	NOUN
hjic-1020	12	17	applications	application	NOUN
hjic-1020	12	18	in	in	ADP
hjic-1020	12	19	bioprocessing	bioprocessing	NOUN
hjic-1020	12	20	and	and	CCONJ
hjic-1020	12	21	chemical	chemical	NOUN
hjic-1020	12	22	engineering	engineering	NOUN
hjic-1020	12	23	consists	consist	VERB
hjic-1020	12	24	in	in	ADP
hjic-1020	12	25	classification	classification	NOUN
hjic-1020	12	26	problems	problem	NOUN
hjic-1020	12	27	,	,	PUNCT
hjic-1020	12	28	like	like	ADP
hjic-1020	12	29	process	process	NOUN
hjic-1020	12	30	fault	fault	VERB
hjic-1020	12	31	detection	detection	NOUN
hjic-1020	12	32	and	and	CCONJ
hjic-1020	12	33	diagnosis	diagnosis	NOUN
hjic-1020	12	34	,	,	PUNCT
hjic-1020	12	35	detection	detection	NOUN
hjic-1020	12	36	and	and	CCONJ
hjic-1020	12	37	location	location	NOUN
hjic-1020	12	38	of	of	ADP
hjic-1020	12	39	gross	gross	ADJ
hjic-1020	12	40	errors	error	NOUN
hjic-1020	12	41	in	in	ADP
hjic-1020	12	42	experimental	experimental	ADJ
hjic-1020	12	43	data	data	NOUN
hjic-1020	12	44	sets	set	NOUN
hjic-1020	12	45	,	,	PUNCT
hjic-1020	12	46	spectral	spectral	ADJ
hjic-1020	12	47	analysis	analysis	NOUN
hjic-1020	12	48	,	,	PUNCT
hjic-1020	12	49	models	model	VERB
hjic-1020	12	50	discrimination	discrimination	NOUN
hjic-1020	12	51	and	and	CCONJ
hjic-1020	12	52	identification	identification	NOUN
hjic-1020	12	53	of	of	ADP
hjic-1020	12	54	model	model	NOUN
hjic-1020	12	55	parameters	parameter	NOUN
hjic-1020	12	56	,	,	PUNCT
hjic-1020	12	57	etc	etc	X
hjic-1020	12	58	.	.	X
hjic-1020	13	1	there	there	PRON
hjic-1020	13	2	are	be	VERB
hjic-1020	13	3	several	several	ADJ
hjic-1020	13	4	types	type	NOUN
hjic-1020	13	5	of	of	ADP
hjic-1020	13	6	neural	neural	ADJ
hjic-1020	13	7	networks	network	NOUN
hjic-1020	13	8	used	use	VERB
hjic-1020	13	9	for	for	ADP
hjic-1020	13	10	classification	classification	NOUN
hjic-1020	13	11	problems	problem	NOUN
hjic-1020	13	12	:	:	PUNCT
hjic-1020	13	13	back	back	ADJ
hjic-1020	13	14	-	-	PUNCT
hjic-1020	13	15	propagation	propagation	NOUN
hjic-1020	13	16	,	,	PUNCT
hjic-1020	13	17	radial	radial	ADJ
hjic-1020	13	18	-	-	PUNCT
hjic-1020	13	19	basis­	basis­	NOUN
hjic-1020	13	20	function	function	NOUN
hjic-1020	13	21	,	,	PUNCT
hjic-1020	13	22	learning	learning	NOUN
hjic-1020	13	23	-	-	PUNCT
hjic-1020	13	24	vector	vector	NOUN
hjic-1020	13	25	-	-	PUNCT
hjic-1020	13	26	quantization	quantization	NOUN
hjic-1020	13	27	,	,	PUNCT
hjic-1020	13	28	probabilistic	probabilistic	ADJ
hjic-1020	13	29	networks	network	NOUN
hjic-1020	13	30	,	,	PUNCT
hjic-1020	13	31	networks	network	NOUN
hjic-1020	13	32	based	base	VERB
hjic-1020	13	33	on	on	ADP
hjic-1020	13	34	adaptive	adaptive	ADJ
hjic-1020	13	35	-	-	PUNCT
hjic-1020	13	36	resonance	resonance	NOUN
hjic-1020	13	37	-	-	PUNCT
hjic-1020	13	38	theory	theory	NOUN
hjic-1020	13	39	,	,	PUNCT
hjic-1020	13	40	and	and	CCONJ
hjic-1020	13	41	so	so	ADV
hjic-1020	13	42	on	on	ADV
hjic-1020	13	43	.	.	PUNCT
hjic-1020	14	1	each	each	DET
hjic-1020	14	2	type	type	NOUN
hjic-1020	14	3	has	have	VERB
hjic-1020	14	4	some	some	DET
hjic-1020	14	5	advantages	advantage	NOUN
hjic-1020	14	6	and	and	CCONJ
hjic-1020	14	7	consequently	consequently	ADV
hjic-1020	14	8	disadvantages	disadvantage	VERB
hjic-1020	14	9	,	,	PUNCT
hjic-1020	14	10	depending	depend	VERB
hjic-1020	14	11	on	on	ADP
hjic-1020	14	12	the	the	DET
hjic-1020	14	13	nature	nature	NOUN
hjic-1020	14	14	of	of	ADP
hjic-1020	14	15	the	the	DET
hjic-1020	14	16	problem	problem	NOUN
hjic-1020	14	17	.	.	PUNCT
hjic-1020	15	1	representatives	representative	NOUN
hjic-1020	15	2	for	for	ADP
hjic-1020	15	3	chemical	chemical	ADJ
hjic-1020	15	4	and	and	CCONJ
hjic-1020	15	5	biochemical	biochemical	ADJ
hjic-1020	15	6	processes	process	NOUN
hjic-1020	15	7	are	be	AUX
hjic-1020	15	8	the	the	DET
hjic-1020	15	9	problems	problem	NOUN
hjic-1020	15	10	with	with	ADP
hjic-1020	15	11	non­	non­	NOUN
hjic-1020	15	12	uniform	uniform	ADJ
hjic-1020	15	13	decision	decision	NOUN
hjic-1020	15	14	regions	region	NOUN
hjic-1020	15	15	where	where	SCONJ
hjic-1020	15	16	the	the	DET
hjic-1020	15	17	data	data	NOUN
hjic-1020	15	18	points	point	NOUN
hjic-1020	15	19	of	of	ADP
hjic-1020	15	20	each	each	DET
hjic-1020	15	21	class	class	NOUN
hjic-1020	15	22	are	be	AUX
hjic-1020	15	23	scattered	scatter	VERB
hjic-1020	15	24	.	.	PUNCT
hjic-1020	16	1	for	for	ADP
hjic-1020	16	2	these	these	DET
hjic-1020	16	3	problems	problem	NOUN
hjic-1020	16	4	radial	radial	ADJ
hjic-1020	16	5	-	-	PUNCT
hjic-1020	16	6	basis­	basis­	NOUN
hjic-1020	16	7	function	function	NOUN
hjic-1020	16	8	and	and	CCONJ
hjic-1020	16	9	back	back	ADJ
hjic-1020	16	10	-	-	PUNCT
hjic-1020	16	11	propagation	propagation	NOUN
hjic-1020	16	12	networks	network	NOUN
hjic-1020	16	13	perform	perform	VERB
hjic-1020	16	14	better	well	ADV
hjic-1020	16	15	[	[	X
hjic-1020	16	16	1,2	1,2	NUM
hjic-1020	16	17	]	]	PUNCT
hjic-1020	16	18	.	.	PUNCT
hjic-1020	17	1	the	the	DET
hjic-1020	17	2	use	use	NOUN
hjic-1020	17	3	of	of	ADP
hjic-1020	17	4	gradient	gradient	NOUN
hjic-1020	17	5	of	of	ADP
hjic-1020	17	6	the	the	DET
hjic-1020	17	7	error	error	NOUN
hjic-1020	17	8	function	function	NOUN
hjic-1020	17	9	during	during	ADP
hjic-1020	17	10	the	the	DET
hjic-1020	17	11	learning	learning	NOUN
hjic-1020	17	12	stage	stage	NOUN
hjic-1020	17	13	affects	affect	VERB
hjic-1020	17	14	the	the	DET
hjic-1020	17	15	performances	performance	NOUN
hjic-1020	17	16	of	of	ADP
hjic-1020	17	17	these	these	DET
hjic-1020	17	18	networks	network	NOUN
hjic-1020	17	19	due	due	ADJ
hjic-1020	17	20	to	to	ADP
hjic-1020	17	21	ending	end	VERB
hjic-1020	17	22	into	into	ADP
hjic-1020	17	23	a	a	DET
hjic-1020	17	24	local	local	ADJ
hjic-1020	17	25	minimum	minimum	NOUN
hjic-1020	17	26	.	.	PUNCT
hjic-1020	18	1	in	in	ADP
hjic-1020	18	2	fact	fact	NOUN
hjic-1020	18	3	,	,	PUNCT
hjic-1020	18	4	the	the	DET
hjic-1020	18	5	gradient	gradient	NOUN
hjic-1020	18	6	technique	technique	NOUN
hjic-1020	18	7	is	be	AUX
hjic-1020	18	8	an	an	DET
hjic-1020	18	9	example	example	NOUN
hjic-1020	18	10	of	of	ADP
hjic-1020	18	11	a	a	DET
hjic-1020	18	12	hill	hill	NOUN
hjic-1020	18	13	-	-	PUNCT
hjic-1020	18	14	climbing	climbing	NOUN
hjic-1020	18	15	~trategy	~trategy	ADP
hjic-1020	18	16	,	,	PUNCT
hjic-1020	18	17	which	which	PRON
hjic-1020	18	18	exploits	exploit	VERB
hjic-1020	18	19	the	the	DET
hjic-1020	18	20	best	good	ADJ
hjic-1020	18	21	solution	solution	NOUN
hjic-1020	18	22	for	for	ADP
hjic-1020	18	23	possible	possible	ADJ
hjic-1020	18	24	improvement	improvement	NOUN
hjic-1020	18	25	;	;	PUNCT
hjic-1020	18	26	on	on	ADP
hjic-1020	18	27	the	the	DET
hjic-1020	18	28	other	other	ADJ
hjic-1020	18	29	hand	hand	NOUN
hjic-1020	18	30	,	,	PUNCT
hjic-1020	18	31	it	it	PRON
hjic-1020	18	32	neglects	neglect	VERB
hjic-1020	18	33	exploration	exploration	NOUN
hjic-1020	18	34	of	of	ADP
hjic-1020	18	35	the	the	DET
hjic-1020	18	36	search	search	NOUN
hjic-1020	18	37	space	space	NOUN
hjic-1020	18	38	.	.	PUNCT
hjic-1020	19	1	random	random	ADJ
hjic-1020	19	2	search	search	NOUN
hjic-1020	19	3	is	be	AUX
hjic-1020	19	4	a	a	DET
hjic-1020	19	5	typical	typical	ADJ
hjic-1020	19	6	example	example	NOUN
hjic-1020	19	7	of	of	ADP
hjic-1020	19	8	a	a	DET
hjic-1020	19	9	strategy	strategy	NOUN
hjic-1020	19	10	,	,	PUNCT
hjic-1020	19	11	which	which	PRON
hjic-1020	19	12	explores	explore	VERB
hjic-1020	19	13	the	the	DET
hjic-1020	19	14	search	search	NOUN
hjic-1020	19	15	space	space	NOUN
hjic-1020	19	16	ignoring	ignore	VERB
hjic-1020	19	17	the	the	DET
hjic-1020	19	18	exploitations	exploitation	NOUN
hjic-1020	19	19	of	of	ADP
hjic-1020	19	20	the	the	DET
hjic-1020	19	21	promising	promising	ADJ
hjic-1020	19	22	regions	region	NOUN
hjic-1020	19	23	of	of	ADP
hjic-1020	19	24	the	the	DET
hjic-1020	19	25	space	space	NOUN
hjic-1020	19	26	.	.	PUNCT
hjic-1020	20	1	for	for	ADP
hjic-1020	20	2	small	small	ADJ
hjic-1020	20	3	spaces	space	NOUN
hjic-1020	20	4	,	,	PUNCT
hjic-1020	20	5	classical	classical	ADJ
hjic-1020	20	6	exhaustive	exhaustive	ADJ
hjic-1020	20	7	methods	method	NOUN
hjic-1020	20	8	usually	usually	ADV
hjic-1020	20	9	suffice	suffice	VERB
hjic-1020	20	10	;	;	PUNCT
hjic-1020	20	11	for	for	ADP
hjic-1020	20	12	larger	large	ADJ
hjic-1020	20	13	spaces	space	NOUN
hjic-1020	20	14	special	special	ADJ
hjic-1020	20	15	artificial	artificial	ADJ
hjic-1020	20	16	intelligence	intelligence	NOUN
hjic-1020	20	17	techniques	technique	NOUN
hjic-1020	20	18	must	must	AUX
hjic-1020	20	19	be	be	AUX
hjic-1020	20	20	employed	employ	VERB
hjic-1020	20	21	.	.	PUNCT
hjic-1020	21	1	genetic	genetic	ADJ
hjic-1020	21	2	algorithms	algorithm	NOUN
hjic-1020	21	3	are	be	AUX
hjic-1020	21	4	among	among	ADP
hjic-1020	21	5	such	such	ADJ
hjic-1020	21	6	techniques	technique	NOUN
hjic-1020	21	7	,	,	PUNCT
hjic-1020	21	8	being	be	AUX
hjic-1020	21	9	a	a	DET
hjic-1020	21	10	class	class	NOUN
hjic-1020	21	11	of	of	ADP
hjic-1020	21	12	general	general	ADJ
hjic-1020	21	13	purpose	purpose	NOUN
hjic-1020	21	14	(	(	PUNCT
hjic-1020	21	15	domain	domain	NOUN
hjic-1020	21	16	independent	independent	ADJ
hjic-1020	21	17	)	)	PUNCT
hjic-1020	21	18	search	search	NOUN
hjic-1020	21	19	methods	method	NOUN
hjic-1020	21	20	which	which	PRON
hjic-1020	21	21	strike	strike	VERB
hjic-1020	21	22	a	a	DET
hjic-1020	21	23	remarkable	remarkable	ADJ
hjic-1020	21	24	balance	balance	NOUN
hjic-1020	21	25	between	between	ADP
hjic-1020	21	26	exploration	exploration	NOUN
hjic-1020	21	27	and	and	CCONJ
hjic-1020	21	28	exploitation	exploitation	NOUN
hjic-1020	21	29	of	of	ADP
hjic-1020	21	30	the	the	DET
hjic-1020	21	31	search	search	NOUN
hjic-1020	21	32	space	space	NOUN
hjic-1020	21	33	[	[	X
hjic-1020	21	34	3	3	NUM
hjic-1020	21	35	]	]	PUNCT
hjic-1020	21	36	.	.	PUNCT
hjic-1020	22	1	for	for	ADP
hjic-1020	22	2	several	several	ADJ
hjic-1020	22	3	years	year	NOUN
hjic-1020	22	4	,	,	PUNCT
hjic-1020	22	5	genetic	genetic	ADJ
hjic-1020	22	6	algorith.ms	algorith.ms	X
hjic-1020	22	7	have	have	AUX
hjic-1020	22	8	been	be	AUX
hjic-1020	22	9	used	use	VERB
hjic-1020	22	10	to	to	PART
hjic-1020	22	11	evolve	evolve	VERB
hjic-1020	22	12	the	the	DET
hjic-1020	22	13	neural	neural	ADJ
hjic-1020	22	14	networks	network	NOUN
hjic-1020	22	15	structure	structure	NOUN
hjic-1020	22	16	.	.	PUNCT
hjic-1020	23	1	as	as	ADV
hjic-1020	23	2	well	well	ADV
hjic-1020	23	3	as	as	ADP
hjic-1020	23	4	the	the	DET
hjic-1020	23	5	weights	weight	NOUN
hjic-1020	23	6	of	of	ADP
hjic-1020	23	7	the	the	DET
hjic-1020	23	8	inter­	inter­	NUM
hjic-1020	23	9	neuronal	neuronal	ADJ
hjic-1020	23	10	connections	connection	NOUN
hjic-1020	23	11	(	(	PUNCT
hjic-1020	23	12	e.g.	e.g.	ADV
hjic-1020	23	13	see	see	VERB
hjic-1020	23	14	(	(	PUNCT
hjic-1020	23	15	4	4	NUM
hjic-1020	23	16	-	-	NUM
hjic-1020	23	17	71	71	NUM
hjic-1020	23	18	)	)	PUNCT
hjic-1020	23	19	.	.	PUNCT
hjic-1020	24	1	the	the	DET
hjic-1020	24	2	aim	aim	NOUN
hjic-1020	24	3	of	of	ADP
hjic-1020	24	4	the	the	DET
hjic-1020	24	5	present	present	ADJ
hjic-1020	24	6	work	work	NOUN
hjic-1020	24	7	consists	consist	VERB
hjic-1020	24	8	in	in	ADP
hjic-1020	24	9	an	an	DET
hjic-1020	24	10	extended	extended	ADJ
hjic-1020	24	11	investigation	investigation	NOUN
hjic-1020	24	12	of	of	ADP
hjic-1020	24	13	the	the	DET
hjic-1020	24	14	features	feature	NOUN
hjic-1020	24	15	of	of	ADP
hjic-1020	24	16	genetic	genetic	ADJ
hjic-1020	24	17	algorithms	algorithm	NOUN
hjic-1020	24	18	in	in	ADP
hjic-1020	24	19	order	order	NOUN
hjic-1020	24	20	to	to	PART
hjic-1020	24	21	realize	realize	VERB
hjic-1020	24	22	a	a	DET
hjic-1020	24	23	high	high	ADJ
hjic-1020	24	24	accurate	accurate	ADJ
hjic-1020	24	25	classification	classification	NOUN
hjic-1020	24	26	neural	neural	ADJ
hjic-1020	24	27	network	network	NOUN
hjic-1020	24	28	with	with	ADP
hjic-1020	24	29	a	a	DET
hjic-1020	24	30	reduced	reduce	VERB
hjic-1020	24	31	number	number	NOUN
hjic-1020	24	32	of	of	ADP
hjic-1020	24	33	neurons	neuron	NOUN
hjic-1020	24	34	.	.	PUNCT
hjic-1020	25	1	methodology	methodology	NOUN
hjic-1020	25	2	the	the	DET
hjic-1020	25	3	investigations	investigation	NOUN
hjic-1020	25	4	were	be	AUX
hjic-1020	25	5	done	do	VERB
hjic-1020	25	6	on	on	ADP
hjic-1020	25	7	the	the	DET
hjic-1020	25	8	three	three	NUM
hjic-1020	25	9	layers	layer	NOUN
hjic-1020	25	10	networks	network	NOUN
hjic-1020	25	11	with	with	ADP
hjic-1020	25	12	linear	linear	ADJ
hjic-1020	25	13	transfer	transfer	NOUN
hjic-1020	25	14	function	function	NOUN
hjic-1020	25	15	for	for	ADP
hjic-1020	25	16	the	the	DET
hjic-1020	25	17	input	input	NOUN
hjic-1020	25	18	layer	layer	NOUN
hjic-1020	25	19	,	,	PUNCT
hjic-1020	25	20	and	and	CCONJ
hjic-1020	25	21	sigmoid	sigmoid	NOUN
hjic-1020	25	22	transfer	transfer	NOUN
hjic-1020	25	23	function	function	NOUN
hjic-1020	25	24	for	for	ADP
hjic-1020	25	25	the	the	DET
hjic-1020	25	26	hidden	hide	VERB
hjic-1020	25	27	and	and	CCONJ
hjic-1020	25	28	output	output	NOUN
hjic-1020	25	29	layers	layer	NOUN
hjic-1020	25	30	.	.	PUNCT
hjic-1020	26	1	because	because	SCONJ
hjic-1020	26	2	the	the	DET
hjic-1020	26	3	number	number	NOUN
hjic-1020	26	4	of	of	ADP
hjic-1020	26	5	neurons	neuron	NOUN
hjic-1020	26	6	in	in	ADP
hjic-1020	26	7	the	the	DET
hjic-1020	26	8	input	input	NOUN
hjic-1020	26	9	layer	layer	NOUN
hjic-1020	26	10	is	be	AUX
hjic-1020	26	11	imposed	impose	VERB
hjic-1020	26	12	by	by	ADP
hjic-1020	26	13	the	the	DET
hjic-1020	26	14	number	number	NOUN
hjic-1020	26	15	of	of	ADP
hjic-1020	26	16	elements	element	NOUN
hjic-1020	26	17	of	of	ADP
hjic-1020	26	18	the	the	DET
hjic-1020	26	19	vectors	vector	NOUN
hjic-1020	26	20	that	that	PRON
hjic-1020	26	21	are	be	AUX
hjic-1020	26	22	classified	classified	ADJ
hjic-1020	26	23	,	,	PUNCT
hjic-1020	26	24	the	the	DET
hjic-1020	26	25	structural	structural	ADJ
hjic-1020	26	26	variables	variable	NOUN
hjic-1020	26	27	of	of	ADP
hjic-1020	26	28	these	these	DET
hjic-1020	26	29	networks	network	NOUN
hjic-1020	26	30	242	242	NUM
hjic-1020	26	31	table	table	NOUN
hjic-1020	26	32	1	1	NUM
hjic-1020	26	33	the	the	DET
hjic-1020	26	34	effects	effect	NOUN
hjic-1020	26	35	of	of	ADP
hjic-1020	26	36	several	several	ADJ
hjic-1020	26	37	factors	factor	NOUN
hjic-1020	26	38	on	on	ADP
hjic-1020	26	39	the	the	DET
hjic-1020	26	40	performances	performance	NOUN
hjic-1020	26	41	of	of	ADP
hjic-1020	26	42	chemical	chemical	ADJ
hjic-1020	26	43	reactor	reactor	NOUN
hjic-1020	26	44	fault	fault	NOUN
hjic-1020	26	45	-	-	PUNCT
hjic-1020	26	46	diagnosis	diagnosis	NOUN
hjic-1020	26	47	network	network	NOUN
hjic-1020	26	48	b	b	PROPN
hjic-1020	26	49	nh	nh	PROPN
hjic-1020	26	50	n0	n0	PROPN
hjic-1020	26	51	rms	rm	NOUN
hjic-1020	26	52	error	error	NOUN
hjic-1020	26	53	percentage	percentage	NOUN
hjic-1020	26	54	of	of	ADP
hjic-1020	26	55	wrong	wrong	ADJ
hjic-1020	26	56	s;p	s;p	PROPN
hjic-1020	26	57	classifications	classification	NOUN
hjic-1020	26	58	(	(	PUNCT
hjic-1020	26	59	%	%	INTJ
hjic-1020	26	60	)	)	PUNCT
hjic-1020	26	61	100	100	NUM
hjic-1020	27	1	[	[	X
hjic-1020	27	2	-100	-100	X
hjic-1020	27	3	;	;	PUNCT
hjic-1020	27	4	100	100	NUM
hjic-1020	27	5	]	]	SYM
hjic-1020	27	6	3	3	NUM
hjic-1020	27	7	4	4	NUM
hjic-1020	27	8	0.3478	0.3478	NUM
hjic-1020	27	9	27.20	27.20	NUM
hjic-1020	27	10	200	200	NUM
hjic-1020	27	11	[	[	X
hjic-1020	27	12	-100	-100	X
hjic-1020	27	13	;	;	PUNCT
hjic-1020	27	14	100	100	NUM
hjic-1020	27	15	]	]	SYM
hjic-1020	27	16	3	3	NUM
hjic-1020	27	17	4	4	NUM
hjic-1020	27	18	0.3418	0.3418	NUM
hjic-1020	27	19	26.25	26.25	NUM
hjic-1020	27	20	500	500	NUM
hjic-1020	27	21	[	[	X
hjic-1020	27	22	-100	-100	X
hjic-1020	27	23	;	;	PUNCT
hjic-1020	27	24	100	100	NUM
hjic-1020	27	25	]	]	SYM
hjic-1020	27	26	3	3	NUM
hjic-1020	27	27	4	4	NUM
hjic-1020	27	28	0.2974	0.2974	NUM
hjic-1020	27	29	25.00	25.00	NUM
hjic-1020	27	30	500	500	NUM
hjic-1020	27	31	[	[	X
hjic-1020	27	32	-50	-50	NUM
hjic-1020	27	33	;	;	PUNCT
hjic-1020	27	34	50	50	NUM
hjic-1020	27	35	]	]	SYM
hjic-1020	27	36	3	3	NUM
hjic-1020	27	37	4	4	NUM
hjic-1020	27	38	0.3827	0.3827	NUM
hjic-1020	27	39	26.25	26.25	NUM
hjic-1020	27	40	500	500	NUM
hjic-1020	28	1	[	[	X
hjic-1020	28	2	-100	-100	X
hjic-1020	28	3	;	;	PUNCT
hjic-1020	28	4	100	100	NUM
hjic-1020	28	5	]	]	SYM
hjic-1020	28	6	3	3	NUM
hjic-1020	28	7	4	4	NUM
hjic-1020	28	8	0.2974	0.2974	NUM
hjic-1020	28	9	25.00	25.00	NUM
hjic-1020	28	10	500	500	NUM
hjic-1020	28	11	[	[	X
hjic-1020	28	12	-250	-250	ADJ
hjic-1020	28	13	;	;	PUNCT
hjic-1020	28	14	250	250	NUM
hjic-1020	28	15	]	]	SYM
hjic-1020	28	16	3	3	NUM
hjic-1020	28	17	4	4	NUM
hjic-1020	28	18	0.4840	0.4840	NUM
hjic-1020	28	19	37.50	37.50	NUM
hjic-1020	28	20	500	500	NUM
hjic-1020	28	21	[	[	X
hjic-1020	28	22	-100	-100	X
hjic-1020	28	23	;	;	PUNCT
hjic-1020	28	24	100	100	NUM
hjic-1020	28	25	]	]	SYM
hjic-1020	28	26	3	3	NUM
hjic-1020	28	27	4	4	NUM
hjic-1020	28	28	0.2974	0.2974	NUM
hjic-1020	28	29	25.00	25.00	NUM
hjic-1020	28	30	500	500	NUM
hjic-1020	28	31	[	[	X
hjic-1020	28	32	-100	-100	X
hjic-1020	28	33	;	;	PUNCT
hjic-1020	28	34	100	100	NUM
hjic-1020	28	35	]	]	SYM
hjic-1020	28	36	5	5	NUM
hjic-1020	28	37	4	4	NUM
hjic-1020	28	38	0.2701	0.2701	NUM
hjic-1020	28	39	21.25	21.25	NUM
hjic-1020	28	40	500	500	NUM
hjic-1020	28	41	[	[	X
hjic-1020	28	42	-100	-100	X
hjic-1020	28	43	;	;	PUNCT
hjic-1020	28	44	100	100	NUM
hjic-1020	28	45	]	]	SYM
hjic-1020	28	46	7	7	NUM
hjic-1020	28	47	4	4	NUM
hjic-1020	28	48	0.1845	0.1845	NUM
hjic-1020	28	49	16.25	16.25	NUM
hjic-1020	28	50	500	500	NUM
hjic-1020	29	1	[	[	X
hjic-1020	29	2	-100	-100	X
hjic-1020	29	3	;	;	PUNCT
hjic-1020	30	1	100	100	NUM
hjic-1020	30	2	]	]	SYM
hjic-1020	30	3	9	9	NUM
hjic-1020	30	4	4	4	NUM
hjic-1020	30	5	0.2395	0.2395	NUM
hjic-1020	30	6	18.75	18.75	NUM
hjic-1020	30	7	500	500	NUM
hjic-1020	30	8	[	[	X
hjic-1020	30	9	-100	-100	X
hjic-1020	30	10	;	;	PUNCT
hjic-1020	30	11	100	100	NUM
hjic-1020	30	12	]	]	SYM
hjic-1020	30	13	3	3	NUM
hjic-1020	30	14	4	4	NUM
hjic-1020	30	15	0.2974	0.2974	NUM
hjic-1020	30	16	25.00	25.00	NUM
hjic-1020	30	17	500	500	NUM
hjic-1020	30	18	[	[	X
hjic-1020	30	19	-100	-100	X
hjic-1020	30	20	;	;	PUNCT
hjic-1020	30	21	100	100	NUM
hjic-1020	30	22	]	]	SYM
hjic-1020	30	23	3	3	NUM
hjic-1020	30	24	3	3	NUM
hjic-1020	30	25	0.1411	0.1411	NUM
hjic-1020	30	26	20.00	20.00	NUM
hjic-1020	30	27	500	500	NUM
hjic-1020	30	28	[	[	X
hjic-1020	30	29	-100	-100	X
hjic-1020	30	30	;	;	PUNCT
hjic-1020	30	31	100	100	NUM
hjic-1020	30	32	]	]	SYM
hjic-1020	30	33	3	3	NUM
hjic-1020	30	34	2	2	NUM
hjic-1020	30	35	0.2398	0.2398	NUM
hjic-1020	30	36	21.25	21.25	NUM
hjic-1020	30	37	equal	equal	ADJ
hjic-1020	30	38	the	the	DET
hjic-1020	30	39	number	number	NOUN
hjic-1020	30	40	of	of	ADP
hjic-1020	30	41	neurons	neuron	NOUN
hjic-1020	30	42	in	in	ADP
hjic-1020	30	43	the	the	DET
hjic-1020	30	44	hidden	hide	VERB
hjic-1020	30	45	and	and	CCONJ
hjic-1020	30	46	output	output	NOUN
hjic-1020	30	47	layers	layer	NOUN
hjic-1020	30	48	.	.	PUNCT
hjic-1020	31	1	the	the	DET
hjic-1020	31	2	number	number	NOUN
hjic-1020	31	3	of	of	ADP
hjic-1020	31	4	neurons	neuron	NOUN
hjic-1020	31	5	in	in	ADP
hjic-1020	31	6	the	the	DET
hjic-1020	31	7	output	output	NOUN
hjic-1020	31	8	layer	layer	NOUN
hjic-1020	31	9	depends	depend	VERB
hjic-1020	31	10	on	on	ADP
hjic-1020	31	11	the	the	DET
hjic-1020	31	12	rule	rule	NOUN
hjic-1020	31	13	of	of	ADP
hjic-1020	31	14	classes	class	NOUN
hjic-1020	31	15	codification	codification	NOUN
hjic-1020	31	16	.	.	PUNCT
hjic-1020	32	1	there	there	PRON
hjic-1020	32	2	are	be	VERB
hjic-1020	32	3	no	no	DET
hjic-1020	32	4	theoretical	theoretical	ADJ
hjic-1020	32	5	guidelines	guideline	NOUN
hjic-1020	32	6	to	to	PART
hjic-1020	32	7	establish	establish	VERB
hjic-1020	32	8	the	the	DET
hjic-1020	32	9	number	number	NOUN
hjic-1020	32	10	of	of	ADP
hjic-1020	32	11	hidden	hidden	ADJ
hjic-1020	32	12	neurons	neuron	NOUN
hjic-1020	32	13	.	.	PUNCT
hjic-1020	33	1	different	different	ADJ
hjic-1020	33	2	from	from	ADP
hjic-1020	33	3	the	the	DET
hjic-1020	33	4	mentioned	mention	VERB
hjic-1020	33	5	works	work	NOUN
hjic-1020	33	6	,	,	PUNCT
hjic-1020	33	7	here	here	ADV
hjic-1020	33	8	each	each	DET
hjic-1020	33	9	chromosome	chromosome	NOUN
hjic-1020	33	10	corresponds	correspond	VERB
hjic-1020	33	11	with	with	ADP
hjic-1020	33	12	a	a	DET
hjic-1020	33	13	complete	complete	ADJ
hjic-1020	33	14	set	set	NOUN
hjic-1020	33	15	of	of	ADP
hjic-1020	33	16	the	the	DET
hjic-1020	33	17	weights	weight	NOUN
hjic-1020	33	18	of	of	ADP
hjic-1020	33	19	the	the	DET
hjic-1020	33	20	inter	inter	ADJ
hjic-1020	33	21	-	-	ADJ
hjic-1020	33	22	neuronal	neuronal	ADJ
hjic-1020	33	23	connections	connection	NOUN
hjic-1020	33	24	for	for	ADP
hjic-1020	33	25	a	a	DET
hjic-1020	33	26	given	give	VERB
hjic-1020	33	27	structure	structure	NOUN
hjic-1020	33	28	of	of	ADP
hjic-1020	33	29	the	the	DET
hjic-1020	33	30	network	network	NOUN
hjic-1020	33	31	(	(	PUNCT
hjic-1020	33	32	respectively	respectively	ADV
hjic-1020	33	33	,	,	PUNCT
hjic-1020	33	34	each	each	DET
hjic-1020	33	35	gene	gene	NOUN
hjic-1020	33	36	represent	represent	VERB
hjic-1020	33	37	a	a	DET
hjic-1020	33	38	weight	weight	NOUN
hjic-1020	33	39	)	)	PUNCT
hjic-1020	33	40	.	.	PUNCT
hjic-1020	34	1	the	the	DET
hjic-1020	34	2	structural	structural	ADJ
hjic-1020	34	3	variables	variable	NOUN
hjic-1020	34	4	,	,	PUNCT
hjic-1020	34	5	respectively	respectively	ADV
hjic-1020	34	6	,	,	PUNCT
hjic-1020	34	7	the	the	DET
hjic-1020	34	8	number	number	NOUN
hjic-1020	34	9	of	of	ADP
hjic-1020	34	10	neurons	neuron	NOUN
hjic-1020	34	11	in	in	ADP
hjic-1020	34	12	the	the	DET
hjic-1020	34	13	hidden	hide	VERB
hjic-1020	34	14	and	and	CCONJ
hjic-1020	34	15	output	output	NOUN
hjic-1020	34	16	layers	layer	NOUN
hjic-1020	34	17	,	,	PUNCT
hjic-1020	34	18	were	be	AUX
hjic-1020	34	19	step	step	NOUN
hjic-1020	34	20	-	-	PUNCT
hjic-1020	34	21	by	by	ADP
hjic-1020	34	22	-	-	PUNCT
hjic-1020	34	23	step	step	NOUN
hjic-1020	34	24	modified	modify	VERB
hjic-1020	34	25	in	in	ADP
hjic-1020	34	26	an	an	DET
hjic-1020	34	27	external	external	ADJ
hjic-1020	34	28	loop	loop	NOUN
hjic-1020	34	29	:	:	PUNCT
hjic-1020	34	30	the	the	DET
hjic-1020	34	31	procedure	procedure	NOUN
hjic-1020	34	32	starts	start	VERB
hjic-1020	34	33	with	with	ADP
hjic-1020	34	34	the	the	DET
hjic-1020	34	35	minimum	minimum	ADJ
hjic-1020	34	36	numbers	number	NOUN
hjic-1020	34	37	of	of	ADP
hjic-1020	34	38	the	the	DET
hjic-1020	34	39	corresponding	correspond	VERB
hjic-1020	34	40	neurons	neuron	NOUN
hjic-1020	34	41	and	and	CCONJ
hjic-1020	34	42	these	these	PRON
hjic-1020	34	43	are	be	AUX
hjic-1020	34	44	progressively	progressively	ADV
hjic-1020	34	45	increased	increase	VERB
hjic-1020	34	46	up	up	ADP
hjic-1020	34	47	to	to	ADP
hjic-1020	34	48	imposed	impose	VERB
hjic-1020	34	49	limits	limit	NOUN
hjic-1020	34	50	.	.	PUNCT
hjic-1020	35	1	the	the	DET
hjic-1020	35	2	genetic	genetic	ADJ
hjic-1020	35	3	algorithm	algorithm	NOUN
hjic-1020	35	4	used	use	VERB
hjic-1020	35	5	is	be	AUX
hjic-1020	35	6	a	a	DET
hjic-1020	35	7	matlab	matlab	PROPN
hjic-1020	35	8	implementation	implementation	NOUN
hjic-1020	35	9	[	[	X
hjic-1020	35	10	8	8	X
hjic-1020	35	11	]	]	PUNCT
hjic-1020	35	12	that	that	PRON
hjic-1020	35	13	can	can	AUX
hjic-1020	35	14	be	be	AUX
hjic-1020	35	15	downloaded	download	VERB
hjic-1020	35	16	at	at	ADP
hjic-1020	35	17	ftp://ftp.eos.ncsu.edu/pub/simul/gaot	ftp://ftp.eos.ncsu.edu/pub/simul/gaot	PROPN
hjic-1020	35	18	.	.	PUNCT
hjic-1020	36	1	float	float	PROPN
hjic-1020	36	2	.	.	PUNCT
hjic-1020	37	1	representation	representation	NOUN
hjic-1020	37	2	of	of	ADP
hjic-1020	37	3	chromosomes	chromosome	NOUN
hjic-1020	37	4	has	have	AUX
hjic-1020	37	5	been	be	AUX
hjic-1020	37	6	used	use	VERB
hjic-1020	37	7	.	.	PUNCT
hjic-1020	38	1	the	the	DET
hjic-1020	38	2	selection	selection	NOUN
hjic-1020	38	3	of	of	ADP
hjic-1020	38	4	candidate	candidate	NOUN
hjic-1020	38	5	chromosomes	chromosome	NOUN
hjic-1020	38	6	for	for	ADP
hjic-1020	38	7	crossover	crossover	NOUN
hjic-1020	38	8	and	and	CCONJ
hjic-1020	38	9	mutation	mutation	NOUN
hjic-1020	38	10	is	be	AUX
hjic-1020	38	11	made	make	VERB
hjic-1020	38	12	according	accord	VERB
hjic-1020	38	13	with	with	ADP
hjic-1020	38	14	a	a	DET
hjic-1020	38	15	ranking	ranking	ADJ
hjic-1020	38	16	selection	selection	NOUN
hjic-1020	38	17	function	function	NOUN
hjic-1020	38	18	based	base	VERB
hjic-1020	38	19	on	on	ADP
hjic-1020	38	20	the	the	DET
hjic-1020	38	21	normalized	normalize	VERB
hjic-1020	38	22	geometric	geometric	ADJ
hjic-1020	38	23	distribution	distribution	NOUN
hjic-1020	38	24	.	.	PUNCT
hjic-1020	39	1	three	three	NUM
hjic-1020	39	2	types	type	NOUN
hjic-1020	39	3	,	,	PUNCT
hjic-1020	39	4	of	of	ADP
hjic-1020	39	5	crossover	crossover	NOUN
hjic-1020	39	6	are	be	AUX
hjic-1020	39	7	applied	apply	VERB
hjic-1020	39	8	:	:	PUNCT
hjic-1020	39	9	simple	simple	ADJ
hjic-1020	39	10	,	,	PUNCT
hjic-1020	39	11	interpolated	interpolate	VERB
hjic-1020	39	12	,	,	PUNCT
hjic-1020	39	13	and	and	CCONJ
hjic-1020	39	14	ext	ext	NOUN
hjic-1020	39	15	:	:	PUNCT
hjic-1020	39	16	rapolated	rapolate	VERB
hjic-1020	39	17	crossover	crossover	NOUN
hjic-1020	39	18	.	.	PUNCT
hjic-1020	40	1	in	in	ADP
hjic-1020	40	2	_	_	PRON
hjic-1020	40	3	the	the	DET
hjic-1020	40	4	simple	simple	ADJ
hjic-1020	40	5	eroesover	eroesover	NOUN
hjic-1020	40	6	,	,	PUNCT
hjic-1020	40	7	the	the	DET
hjic-1020	40	8	crossover	crossover	NOUN
hjic-1020	40	9	point	point	NOUN
hjic-1020	40	10	is	be	AUX
hjic-1020	40	11	randomly	randomly	ADV
hjic-1020	40	12	selected	select	VERB
hjic-1020	40	13	.	.	PUNCT
hjic-1020	41	1	the	the	DET
hjic-1020	41	2	~lated	~lated	PROPN
hjic-1020	41	3	c.roowve	c.roowve	NOUN
hjic-1020	41	4	:	:	PUNCT
hjic-1020	41	5	r	r	NOUN
hjic-1020	41	6	performs	perform	VERB
hjic-1020	41	7	an	an	DET
hjic-1020	41	8	interpolation	interpolation	NOUN
hjic-1020	41	9	along	along	ADP
hjic-1020	41	10	the	the	DET
hjic-1020	41	11	line	line	NOUN
hjic-1020	41	12	formed	form	VERB
hjic-1020	41	13	by	by	ADP
hjic-1020	41	14	the	the	DET
hjic-1020	41	15	two	two	NUM
hjic-1020	41	16	parents	parent	NOUN
hjic-1020	41	17	.	.	PUNCT
hjic-1020	42	1	the	the	DET
hjic-1020	42	2	extrapolated	extrapolate	VERB
hjic-1020	42	3	~ves	~ve	NOUN
hjic-1020	42	4	performs	perform	VERB
hjic-1020	42	5	an	an	DET
hjic-1020	42	6	extrapolation	extrapolation	NOUN
hjic-1020	42	7	along	along	ADP
hjic-1020	42	8	the	the	DET
hjic-1020	42	9	line	line	NOUN
hjic-1020	42	10	fonned	fonne	VERB
hjic-1020	42	11	by	by	ADP
hjic-1020	42	12	the	the	DET
hjic-1020	42	13	two	two	NUM
hjic-1020	42	14	parents	parent	NOUN
hjic-1020	42	15	in	in	ADP
hjic-1020	42	16	the	the	DET
hjic-1020	42	17	direction	direction	NOUN
hjic-1020	42	18	of	of	ADP
hjic-1020	42	19	better	well	ADJ
hjic-1020	42	20	parent	parent	NOUN
hjic-1020	42	21	.	.	PUNCT
hjic-1020	43	1	four	four	NUM
hjic-1020	43	2	types	type	NOUN
hjic-1020	43	3	of	of	ADP
hjic-1020	43	4	mutation	mutation	NOUN
hjic-1020	43	5	are	be	AUX
hjic-1020	43	6	applied	apply	VERB
hjic-1020	43	7	:	:	PUNCT
hjic-1020	43	8	boundary	boundary	ADJ
hjic-1020	43	9	,	,	PUNCT
hjic-1020	43	10	multi	multi	ADJ
hjic-1020	43	11	-	-	ADJ
hjic-1020	43	12	nonuniform	nonuniform	ADJ
hjic-1020	43	13	.	.	PUNCT
hjic-1020	43	14	nonuniform	nonuniform	NOUN
hjic-1020	43	15	,	,	PUNCT
hjic-1020	43	16	and	and	CCONJ
hjic-1020	43	17	uniform	uniform	ADJ
hjic-1020	43	18	mutation	mutation	NOUN
hjic-1020	43	19	.	.	PUNCT
hjic-1020	44	1	boundary	boundary	ADJ
hjic-1020	44	2	mutation	mutation	NOUN
hjic-1020	44	3	changes	change	VERB
hjic-1020	44	4	one	one	NUM
hjic-1020	44	5	gene	gene	NOUN
hjic-1020	44	6	of	of	ADP
hjic-1020	44	7	the	the	DET
hjic-1020	44	8	selected	select	VERB
hjic-1020	44	9	chromosome	chromosome	NOUN
hjic-1020	44	10	randomly	randomly	ADV
hjic-1020	44	11	either	either	CCONJ
hjic-1020	44	12	to	to	ADP
hjic-1020	44	13	its	its	PRON
hjic-1020	44	14	upper	upper	ADJ
hjic-1020	44	15	or	or	CCONJ
hjic-1020	44	16	lower	lower	ADV
hjic-1020	44	17	bound	bind	VERB
hjic-1020	44	18	.	.	PUNCT
hjic-1020	45	1	multi	multi	ADJ
hjic-1020	45	2	-	-	ADJ
hjic-1020	45	3	nonuniform	nonuniform	ADJ
hjic-1020	45	4	mutation	mutation	NOUN
hjic-1020	45	5	changes	change	NOUN
hjic-1020	45	6	au	au	VERB
hjic-1020	45	7	genes	gene	NOUN
hjic-1020	45	8	.	.	PUNCT
hjic-1020	46	1	whereas	whereas	SCONJ
hjic-1020	46	2	nonuniform	nonuniform	ADJ
hjic-1020	46	3	mutation	mutation	NOUN
hjic-1020	46	4	changes	change	VERB
hjic-1020	46	5	one	one	NUM
hjic-1020	46	6	of	of	ADP
hjic-1020	46	7	the	the	DET
hjic-1020	46	8	genes	gene	NOUN
hjic-1020	46	9	in	in	ADP
hjic-1020	46	10	a	a	DET
hjic-1020	46	11	chromosome	chromosome	NOUN
hjic-1020	46	12	on	on	ADP
hjic-1020	46	13	the	the	DET
hjic-1020	46	14	base	base	NOUN
hjic-1020	46	15	of	of	ADP
hjic-1020	46	16	a	a	DET
hjic-1020	46	17	non	non	ADJ
hjic-1020	46	18	-	-	ADJ
hjic-1020	46	19	uniform	uniform	ADJ
hjic-1020	46	20	probability	probability	NOUN
hjic-1020	46	21	distribution	distribution	NOUN
hjic-1020	46	22	.	.	PUNCT
hjic-1020	47	1	this	this	DET
hjic-1020	47	2	gaussian	gaussian	ADJ
hjic-1020	47	3	distribution	distribution	NOUN
hjic-1020	47	4	starts	start	VERB
hjic-1020	47	5	wide~	wide~	NOUN
hjic-1020	47	6	and	and	CCONJ
hjic-1020	47	7	narrows	narrow	VERB
hjic-1020	47	8	to	to	ADP
hjic-1020	47	9	a	a	DET
hjic-1020	47	10	point	point	NOUN
hjic-1020	47	11	distribution	distribution	NOUN
hjic-1020	47	12	as	as	ADP
hjic-1020	47	13	the	the	DET
hjic-1020	47	14	current	current	ADJ
hjic-1020	47	15	generation	generation	NOUN
hjic-1020	47	16	approaches	approach	NOUN
hjic-1020	47	17	to	to	ADP
hjic-1020	47	18	the	the	DET
hjic-1020	47	19	maximum	maximum	ADJ
hjic-1020	47	20	generation	generation	NOUN
hjic-1020	47	21	.	.	PUNCT
hjic-1020	48	1	uniform	uniform	ADJ
hjic-1020	48	2	mutation	mutation	NOUN
hjic-1020	48	3	changes	change	VERB
hjic-1020	48	4	one	one	NUM
hjic-1020	48	5	of	of	ADP
hjic-1020	48	6	the	the	DET
hjic-1020	48	7	genes	gene	NOUN
hjic-1020	48	8	based	base	VERB
hjic-1020	48	9	on	on	ADP
hjic-1020	48	10	a	a	DET
hjic-1020	48	11	uniform	uniform	ADJ
hjic-1020	48	12	probability	probability	NOUN
hjic-1020	48	13	distribution	distribution	NOUN
hjic-1020	48	14	.	.	PUNCT
hjic-1020	49	1	the	the	DET
hjic-1020	49	2	numbers	number	NOUN
hjic-1020	49	3	of	of	ADP
hjic-1020	49	4	applications	application	NOUN
hjic-1020	49	5	of	of	ADP
hjic-1020	49	6	the	the	DET
hjic-1020	49	7	different	different	ADJ
hjic-1020	49	8	crossover	crossover	NOUN
hjic-1020	49	9	and	and	CCONJ
hjic-1020	49	10	mutati	mutati	NUM
hjic-1020	49	11	~	~	NOUN
hjic-1020	49	12	n	n	PRON
hjic-1020	49	13	operators	operator	NOUN
hjic-1020	49	14	are	be	AUX
hjic-1020	49	15	imposed	impose	VERB
hjic-1020	49	16	as	as	ADP
hjic-1020	49	17	parameters	parameter	NOUN
hjic-1020	49	18	of	of	ADP
hjic-1020	49	19	the	the	DET
hjic-1020	49	20	genetic	genetic	ADJ
hjic-1020	49	21	algorithm	algorithm	NOUN
hjic-1020	49	22	.	.	PUNCT
hjic-1020	50	1	the	the	DET
hjic-1020	50	2	investigation	investigation	NOUN
hjic-1020	50	3	of	of	ADP
hjic-1020	50	4	the	the	DET
hjic-1020	50	5	effects	effect	NOUN
hjic-1020	50	6	of	of	ADP
hjic-1020	50	7	these	these	DET
hjic-1020	50	8	parameters	parameter	NOUN
hjic-1020	50	9	was	be	AUX
hjic-1020	50	10	out	out	ADP
hjic-1020	50	11	of	of	ADP
hjic-1020	50	12	the	the	DET
hjic-1020	50	13	aims	aim	NOUN
hjic-1020	50	14	of	of	ADP
hjic-1020	50	15	the	the	DET
hjic-1020	50	16	present	present	ADJ
hjic-1020	50	17	work	work	NOUN
hjic-1020	50	18	,	,	PUNCT
hjic-1020	50	19	and	and	CCONJ
hjic-1020	50	20	their	their	PRON
hjic-1020	50	21	default	default	NOUN
hjic-1020	50	22	·	·	PUNCT
hjic-1020	50	23	values	value	NOUN
hjic-1020	50	24	have	have	AUX
hjic-1020	50	25	been	be	AUX
hjic-1020	50	26	used	use	VERB
hjic-1020	50	27	,	,	PUNCT
hjic-1020	50	28	respectively	respectively	ADV
hjic-1020	50	29	for	for	ADP
hjic-1020	50	30	each	each	DET
hjic-1020	50	31	generation	generation	NOUN
hjic-1020	50	32	:	:	PUNCT
hjic-1020	50	33	2	2	NUM
hjic-1020	50	34	simple	simple	ADJ
hjic-1020	50	35	,	,	PUNCT
hjic-1020	50	36	2	2	NUM
hjic-1020	50	37	interpolated	interpolate	VERB
hjic-1020	50	38	,	,	PUNCT
hjic-1020	50	39	and	and	CCONJ
hjic-1020	50	40	2	2	NUM
hjic-1020	50	41	extrapolated	extrapolate	VERB
hjic-1020	50	42	crossover	crossover	NOUN
hjic-1020	50	43	,	,	PUNCT
hjic-1020	50	44	4	4	NUM
hjic-1020	50	45	boundary	boundary	ADJ
hjic-1020	50	46	,	,	PUNCT
hjic-1020	50	47	6	6	NUM
hjic-1020	50	48	multi	multi	ADJ
hjic-1020	50	49	-	-	ADJ
hjic-1020	50	50	nonuniform	nonuniform	ADJ
hjic-1020	50	51	,	,	PUNCT
hjic-1020	50	52	4	4	NUM
hjic-1020	50	53	nonuniform	nonuniform	NOUN
hjic-1020	50	54	,	,	PUNCT
hjic-1020	50	55	and	and	CCONJ
hjic-1020	50	56	4	4	NUM
hjic-1020	50	57	uniform	uniform	NOUN
hjic-1020	50	58	mutation	mutation	NOUN
hjic-1020	50	59	.	.	PUNCT
hjic-1020	51	1	due	due	ADP
hjic-1020	51	2	to	to	ADP
hjic-1020	51	3	the	the	DET
hjic-1020	51	4	use	use	NOUN
hjic-1020	51	5	of	of	ADP
hjic-1020	51	6	a	a	DET
hjic-1020	51	7	maximization	maximization	NOUN
hjic-1020	51	8	algorithm	algorithm	NOUN
hjic-1020	51	9	,	,	PUNCT
hjic-1020	51	10	the	the	DET
hjic-1020	51	11	chromosome	chromosome	NOUN
hjic-1020	51	12	fitness	fitness	NOUN
hjic-1020	51	13	corresponds	correspond	VERB
hjic-1020	51	14	to	to	ADP
hjic-1020	51	15	the	the	DET
hjic-1020	51	16	negative	negative	ADJ
hjic-1020	51	17	value	value	NOUN
hjic-1020	51	18	of	of	ADP
hjic-1020	51	19	rms	rm	NOUN
hjic-1020	51	20	error	error	NOUN
hjic-1020	51	21	.	.	PUNCT
hjic-1020	52	1	the	the	DET
hjic-1020	52	2	attempts	attempt	NOUN
hjic-1020	52	3	to	to	PART
hjic-1020	52	4	use	use	VERB
hjic-1020	52	5	o	o	NOUN
hjic-1020	52	6	~	~	NOUN
hjic-1020	52	7	her	her	PRON
hjic-1020	52	8	expressions	expression	NOUN
hjic-1020	52	9	for	for	ADP
hjic-1020	52	10	chromosome	chromosome	NOUN
hjic-1020	52	11	fitness	fitness	NOUN
hjic-1020	52	12	(	(	PUNCT
hjic-1020	52	13	e.g.	e.g.	ADV
hjic-1020	52	14	the	the	DET
hjic-1020	52	15	average	average	ADJ
hjic-1020	52	16	relative	relative	ADJ
hjic-1020	52	17	error	error	NOUN
hjic-1020	52	18	)	)	PUNCT
hjic-1020	52	19	did	do	AUX
hjic-1020	52	20	not	not	PART
hjic-1020	52	21	give	give	VERB
hjic-1020	52	22	meaningful	meaningful	ADJ
hjic-1020	52	23	improved	improved	ADJ
hjic-1020	52	24	results	result	NOUN
hjic-1020	52	25	.	.	PUNCT
hjic-1020	53	1	applications	application	NOUN
hjic-1020	53	2	chemical	chemical	NOUN
hjic-1020	53	3	reactor	reactor	NOUN
hjic-1020	53	4	fault	fault	NOUN
hjic-1020	53	5	-	-	PUNCT
hjic-1020	53	6	diagnosis	diagnosis	NOUN
hjic-1020	53	7	this	this	DET
hjic-1020	53	8	application	application	NOUN
hjic-1020	53	9	and	and	CCONJ
hjic-1020	53	10	the	the	DET
hjic-1020	53	11	corresponding	corresponding	ADJ
hjic-1020	53	12	data	datum	NOUN
hjic-1020	53	13	are	be	AUX
hjic-1020	53	14	presented	present	VERB
hjic-1020	53	15	in	in	ADP
hjic-1020	53	16	[	[	X
hjic-1020	53	17	9	9	NUM
hjic-1020	53	18	]	]	PUNCT
hjic-1020	53	19	.	.	PUNCT
hjic-1020	54	1	the	the	DET
hjic-1020	54	2	input	input	NOUN
hjic-1020	54	3	vector	vector	NOUN
hjic-1020	54	4	contains	contain	VERB
hjic-1020	54	5	reactor	reactor	NOUN
hjic-1020	54	6	inlet	inlet	NOUN
hjic-1020	54	7	temperature	temperature	NOUN
hjic-1020	54	8	,	,	PUNCT
hjic-1020	54	9	reactor	reactor	NOUN
hjic-1020	54	10	inlet	inlet	NOUN
hjic-1020	54	11	pressure	pressure	NOUN
hjic-1020	54	12	,	,	PUNCT
hjic-1020	54	13	and	and	CCONJ
hjic-1020	54	14	feed	feed	VERB
hjic-1020	54	15	flowrate	flowrate	ADJ
hjic-1020	54	16	.	.	PUNCT
hjic-1020	55	1	the	the	DET
hjic-1020	55	2	elements	element	NOUN
hjic-1020	55	3	of	of	ADP
hjic-1020	55	4	the	the	DET
hjic-1020	55	5	output	output	NOUN
hjic-1020	55	6	vector	vector	NOUN
hjic-1020	55	7	are	be	AUX
hjic-1020	55	8	three	three	NUM
hjic-1020	55	9	fault	fault	NOUN
hjic-1020	55	10	classes	class	NOUN
hjic-1020	55	11	:	:	PUNCT
hjic-1020	55	12	low	low	ADJ
hjic-1020	55	13	conversion	conversion	NOUN
hjic-1020	55	14	,	,	PUNCT
hjic-1020	55	15	low	low	ADJ
hjic-1020	55	16	catalyst	catalyst	NOUN
hjic-1020	55	17	selectivity	selectivity	NOUN
hjic-1020	55	18	,	,	PUNCT
hjic-1020	55	19	and	and	CCONJ
hjic-1020	55	20	catalyst	catalyst	NOUN
hjic-1020	55	21	sintering	sintering	NOUN
hjic-1020	55	22	.	.	PUNCT
hjic-1020	56	1	the	the	DET
hjic-1020	56	2	following	follow	VERB
hjic-1020	56	3	factors	factor	NOUN
hjic-1020	56	4	were	be	AUX
hjic-1020	56	5	studied	study	VERB
hjic-1020	56	6	:	:	PUNCT
hjic-1020	56	7	a	a	X
hjic-1020	56	8	)	)	PUNCT
hjic-1020	56	9	the	the	DET
hjic-1020	56	10	size	size	NOUN
hjic-1020	56	11	of	of	ADP
hjic-1020	56	12	initial	initial	ADJ
hjic-1020	56	13	population	population	NOUN
hjic-1020	56	14	(	(	PUNCT
hjic-1020	56	15	sip	sip	PROPN
hjic-1020	56	16	)	)	PUNCT
hjic-1020	56	17	;	;	PUNCT
hjic-1020	56	18	b	b	X
hjic-1020	56	19	)	)	PUNCT
hjic-1020	56	20	the	the	DET
hjic-1020	56	21	bounds	bound	NOUN
hjic-1020	56	22	imposed	impose	VERB
hjic-1020	56	23	to	to	ADP
hjic-1020	56	24	the	the	DET
hjic-1020	56	25	weights	weight	NOUN
hjic-1020	56	26	of	of	ADP
hjic-1020	56	27	the	the	DET
hjic-1020	56	28	inter­	inter­	NUM
hjic-1020	56	29	neuronal	neuronal	ADJ
hjic-1020	56	30	connections	connection	NOUN
hjic-1020	56	31	(	(	PUNCT
hjic-1020	56	32	b	b	NOUN
hjic-1020	56	33	)	)	PUNCT
hjic-1020	56	34	;	;	PUNCT
hjic-1020	56	35	c	c	X
hjic-1020	56	36	)	)	PUNCT
hjic-1020	56	37	the	the	DET
hjic-1020	56	38	number	number	NOUN
hjic-1020	56	39	of	of	ADP
hjic-1020	56	40	neurons	neuron	NOUN
hjic-1020	56	41	in	in	ADP
hjic-1020	56	42	the	the	DET
hjic-1020	56	43	hidden	hide	VERB
hjic-1020	56	44	layer	layer	NOUN
hjic-1020	56	45	(	(	PUNCT
hjic-1020	56	46	nh	nh	NOUN
hjic-1020	56	47	)	)	PUNCT
hjic-1020	56	48	;	;	PUNCT
hjic-1020	56	49	d	d	X
hjic-1020	56	50	)	)	PUNCT
hjic-1020	56	51	the.number	the.number	PRON
hjic-1020	56	52	of	of	ADP
hjic-1020	56	53	neurons	neuron	NOUN
hjic-1020	56	54	in	in	ADP
hjic-1020	56	55	the	the	DET
hjic-1020	56	56	output	output	NOUN
hjic-1020	56	57	layer	layer	NOUN
hjic-1020	56	58	(	(	PUNCT
hjic-1020	56	59	no	no	INTJ
hjic-1020	56	60	)	)	PUNCT
hjic-1020	56	61	;	;	PUNCT
hjic-1020	56	62	the	the	DET
hjic-1020	56	63	last	last	ADJ
hjic-1020	56	64	factor	factor	NOUN
hjic-1020	56	65	corresponds	correspond	VERB
hjic-1020	56	66	to	to	ADP
hjic-1020	56	67	different	different	ADJ
hjic-1020	56	68	rules	rule	NOUN
hjic-1020	56	69	of	of	ADP
hjic-1020	56	70	classes	class	NOUN
hjic-1020	56	71	codification	codification	NOUN
hjic-1020	56	72	.	.	PUNCT
hjic-1020	57	1	there	there	PRON
hjic-1020	57	2	are	be	VERB
hjic-1020	57	3	four	four	NUM
hjic-1020	57	4	types	type	NOUN
hjic-1020	57	5	of	of	ADP
hjic-1020	57	6	outputs	output	NOUN
hjic-1020	57	7	,	,	PUNCT
hjic-1020	57	8	corresponding	correspond	VERB
hjic-1020	57	9	to	to	ADP
hjic-1020	57	10	four	four	NUM
hjic-1020	57	11	classes	class	NOUN
hjic-1020	57	12	:	:	PUNCT
hjic-1020	57	13	the	the	DET
hjic-1020	57	14	correct	correct	ADJ
hjic-1020	57	15	operating	operating	NOUN
hjic-1020	57	16	regime	regime	NOUN
hjic-1020	57	17	,	,	PUNCT
hjic-1020	57	18	and	and	CCONJ
hjic-1020	57	19	the	the	DET
hjic-1020	57	20	three	three	NUM
hjic-1020	57	21	fault	fault	NOUN
hjic-1020	57	22	regimes	regime	NOUN
hjic-1020	57	23	.	.	PUNCT
hjic-1020	58	1	~he	~he	NOUN
hjic-1020	58	2	classical	classical	ADJ
hjic-1020	58	3	codification	codification	NOUN
hjic-1020	58	4	corresponds	correspond	VERB
hjic-1020	58	5	to	to	ADP
hjic-1020	58	6	the	the	DET
hjic-1020	58	7	use	use	NOUN
hjic-1020	58	8	of	of	ADP
hjic-1020	58	9	four	four	NUM
hjic-1020	58	10	output	output	NOUN
hjic-1020	58	11	neurons	neuron	NOUN
hjic-1020	58	12	,	,	PUNCT
hjic-1020	58	13	as	as	SCONJ
hjic-1020	58	14	follows	follow	VERB
hjic-1020	58	15	:	:	PUNCT
hjic-1020	59	1	[	[	X
hjic-1020	59	2	1	1	NUM
hjic-1020	59	3	0	0	NUM
hjic-1020	59	4	0	0	NUM
hjic-1020	59	5	0	0	NUM
hjic-1020	59	6	]	]	PUNCT
hjic-1020	59	7	;	;	PUNCT
hjic-1020	59	8	[	[	X
hjic-1020	59	9	0	0	NUM
hjic-1020	59	10	1	1	NUM
hjic-1020	59	11	0	0	NUM
hjic-1020	59	12	0	0	NUM
hjic-1020	59	13	]	]	PUNCT
hjic-1020	59	14	;	;	PUNCT
hjic-1020	60	1	[	[	X
hjic-1020	60	2	0	0	NUM
hjic-1020	60	3	0	0	NUM
hjic-1020	60	4	1	1	NUM
hjic-1020	60	5	0	0	NUM
hjic-1020	60	6	]	]	PUNCT
hjic-1020	60	7	;	;	PUNCT
hjic-1020	60	8	[	[	X
hjic-1020	60	9	0	0	NUM
hjic-1020	60	10	0	0	NUM
hjic-1020	60	11	01	01	NUM
hjic-1020	60	12	]	]	PUNCT
hjic-1020	60	13	.	.	PUNCT
hjic-1020	61	1	also	also	ADV
hjic-1020	61	2	,	,	PUNCT
hjic-1020	61	3	there	there	PRON
hjic-1020	61	4	are	be	VERB
hjic-1020	61	5	possible	possible	ADJ
hjic-1020	61	6	other	other	ADJ
hjic-1020	61	7	two	two	NUM
hjic-1020	61	8	codification	codification	NOUN
hjic-1020	61	9	methods	method	NOUN
hjic-1020	61	10	:	:	PUNCT
hjic-1020	61	11	a	a	DET
hjic-1020	61	12	codification	codification	NOUN
hjic-1020	61	13	using	use	VERB
hjic-1020	61	14	three	three	NUM
hjic-1020	61	15	output	output	NOUN
hjic-1020	61	16	neurons	neuron	NOUN
hjic-1020	61	17	,	,	PUNCT
hjic-1020	61	18	respectively	respectively	ADV
hjic-1020	61	19	:	:	PUNCT
hjic-1020	62	1	[	[	X
hjic-1020	62	2	0	0	NUM
hjic-1020	62	3	0	0	NUM
hjic-1020	62	4	0	0	NUM
hjic-1020	62	5	]	]	PUNCT
hjic-1020	62	6	;	;	PUNCT
hjic-1020	62	7	[	[	X
hjic-1020	62	8	1	1	NUM
hjic-1020	62	9	0	0	NUM
hjic-1020	62	10	0	0	NUM
hjic-1020	62	11	]	]	PUNCT
hjic-1020	62	12	;	;	PUNCT
hjic-1020	63	1	[	[	X
hjic-1020	63	2	0	0	NUM
hjic-1020	63	3	1	1	NUM
hjic-1020	63	4	0	0	NUM
hjic-1020	63	5	]	]	PUNCT
hjic-1020	63	6	;	;	PUNCT
hjic-1020	64	1	[	[	X
hjic-1020	64	2	0	0	NUM
hjic-1020	64	3	0	0	NUM
hjic-1020	64	4	1	1	NUM
hjic-1020	64	5	]	]	PUNCT
hjic-1020	64	6	,	,	PUNCT
hjic-1020	64	7	and	and	CCONJ
hjic-1020	64	8	a	a	DET
hjic-1020	64	9	codification	codification	NOUN
hjic-1020	64	10	with	with	ADP
hjic-1020	64	11	two	two	NUM
hjic-1020	64	12	output	output	NOUN
hjic-1020	64	13	neurons	neuron	NOUN
hjic-1020	64	14	:	:	PUNCT
hjic-1020	65	1	[	[	X
hjic-1020	65	2	0	0	NUM
hjic-1020	65	3	0	0	NUM
hjic-1020	65	4	]	]	PUNCT
hjic-1020	65	5	;	;	PUNCT
hjic-1020	66	1	[	[	X
hjic-1020	66	2	0	0	NUM
hjic-1020	66	3	1	1	NUM
hjic-1020	66	4	]	]	PUNCT
hjic-1020	66	5	;	;	PUNCT
hjic-1020	66	6	[	[	X
hjic-1020	66	7	1	1	NUM
hjic-1020	66	8	0	0	NUM
hjic-1020	66	9	]	]	PUNCT
hjic-1020	66	10	;	;	PUNCT
hjic-1020	66	11	[	[	X
hjic-1020	66	12	1	1	NUM
hjic-1020	66	13	1	1	NUM
hjic-1020	66	14	]	]	PUNCT
hjic-1020	66	15	.	.	PUNCT
hjic-1020	67	1	the	the	DET
hjic-1020	67	2	last	last	ADJ
hjic-1020	67	3	codification	codification	NOUN
hjic-1020	67	4	is	be	AUX
hjic-1020	67	5	in	in	ADP
hjic-1020	67	6	fact	fact	NOUN
hjic-1020	67	7	the	the	DET
hjic-1020	67	8	binary	binary	ADJ
hjic-1020	67	9	representation	representation	NOUN
hjic-1020	67	10	of	of	ADP
hjic-1020	67	11	each	each	DET
hjic-1020	67	12	class	class	NOUN
hjic-1020	67	13	number	number	NOUN
hjic-1020	67	14	.	.	PUNCT
hjic-1020	68	1	our	our	PRON
hjic-1020	68	2	investigations	investigation	NOUN
hjic-1020	68	3	in	in	ADP
hjic-1020	68	4	the	the	DET
hjic-1020	68	5	field	field	NOUN
hjic-1020	68	6	of	of	ADP
hjic-1020	68	7	classification	classification	NOUN
hjic-1020	68	8	neural	neural	ADJ
hjic-1020	68	9	networks	network	NOUN
hjic-1020	68	10	indicated	indicate	VERB
hjic-1020	68	11	that	that	SCONJ
hjic-1020	68	12	the	the	DET
hjic-1020	68	13	activity	activity	NOUN
hjic-1020	68	14	and	and	CCONJ
hjic-1020	68	15	corresponding	corresponding	ADJ
hjic-1020	68	16	response	response	NOUN
hjic-1020	68	17	of	of	ADP
hjic-1020	68	18	the	the	DET
hjic-1020	68	19	output	output	NOUN
hjic-1020	68	20	neurones	neurone	NOUN
hjic-1020	68	21	must	must	AUX
hjic-1020	68	22	close	close	VERB
hjic-1020	68	23	to	to	ADP
hjic-1020	68	24	0	0	NUM
hjic-1020	68	25	or	or	CCONJ
hjic-1020	68	26	1	1	NUM
hjic-1020	68	27	.	.	PUNCT
hjic-1020	69	1	a	a	DET
hjic-1020	69	2	neural	neural	ADJ
hjic-1020	69	3	network	network	NOUN
hjic-1020	69	4	with	with	ADP
hjic-1020	69	5	one	one	NUM
hjic-1020	69	6	output	output	NOUN
hjic-1020	69	7	neuron	neuron	NOUN
hjic-1020	69	8	(	(	PUNCT
hjic-1020	69	9	having	have	VERB
hjic-1020	69	10	the	the	DET
hjic-1020	69	11	smallest	small	ADJ
hjic-1020	69	12	number	number	NOUN
hjic-1020	69	13	of	of	ADP
hjic-1020	69	14	the	the	DET
hjic-1020	69	15	inter	inter	ADJ
hjic-1020	69	16	-	-	ADJ
hjic-1020	69	17	neuronal	neuronal	ADJ
hjic-1020	69	18	connections	connection	NOUN
hjic-1020	69	19	weights	weight	NOUN
hjic-1020	69	20	)	)	PUNCT
hjic-1020	69	21	for	for	ADP
hjic-1020	69	22	which	which	PRON
hjic-1020	69	23	the	the	DET
hjic-1020	69	24	above	above	ADJ
hjic-1020	69	25	mentioned	mention	VERB
hjic-1020	69	26	4	4	NUM
hjic-1020	69	27	classes	class	NOUN
hjic-1020	69	28	are	be	AUX
hjic-1020	69	29	codified	codify	VERB
hjic-1020	69	30	with	with	ADP
hjic-1020	69	31	the	the	DET
hjic-1020	69	32	activity	activity	NOUN
hjic-1020	69	33	domains	domain	NOUN
hjic-1020	69	34	:	:	PUNCT
hjic-1020	69	35	0	0	NUM
hjic-1020	69	36	-	-	SYM
hjic-1020	69	37	0.25	0.25	NUM
hjic-1020	69	38	;	;	PUNCT
hjic-1020	69	39	0.25	0.25	NUM
hjic-1020	69	40	-	-	SYM
hjic-1020	69	41	0.5	0.5	NUM
hjic-1020	69	42	;	;	PUNCT
hjic-1020	69	43	0.50.75	0.50.75	PROPN
hjic-1020	69	44	;	;	PUNCT
hjic-1020	69	45	0.75	0.75	NUM
hjic-1020	69	46	-	-	SYM
hjic-1020	69	47	1	1	NUM
hjic-1020	69	48	can	can	AUX
hjic-1020	69	49	not	not	PART
hjic-1020	69	50	be	be	AUX
hjic-1020	69	51	trained	train	VERB
hjic-1020	69	52	to	to	PART
hjic-1020	69	53	give	give	VERB
hjic-1020	69	54	a	a	DET
hjic-1020	69	55	correct	correct	ADJ
hjic-1020	69	56	classification	classification	NOUN
hjic-1020	69	57	.	.	PUNCT
hjic-1020	70	1	due	due	ADP
hjic-1020	70	2	to	to	ADP
hjic-1020	70	3	the	the	DET
hjic-1020	70	4	random	random	ADJ
hjic-1020	70	5	generation	generation	NOUN
hjic-1020	70	6	of	of	ADP
hjic-1020	70	7	the	the	DET
hjic-1020	70	8	initial	initial	ADJ
hjic-1020	70	9	population	population	NOUN
hjic-1020	70	10	of	of	ADP
hjic-1020	70	11	the	the	DET
hjic-1020	70	12	genetic	genetic	ADJ
hjic-1020	70	13	algorithm	algorithm	NOUN
hjic-1020	70	14	,	,	PUNCT
hjic-1020	70	15	each	each	DET
hjic-1020	70	16	test	test	NOUN
hjic-1020	70	17	was	be	AUX
hjic-1020	70	18	repeated	repeat	VERB
hjic-1020	70	19	five	five	NUM
hjic-1020	70	20	times	time	NOUN
hjic-1020	70	21	.	.	PUNCT
hjic-1020	71	1	the	the	DET
hjic-1020	71	2	results	result	NOUN
hjic-1020	71	3	presented	present	VERB
hjic-1020	71	4	in	in	ADP
hjic-1020	71	5	the	the	DET
hjic-1020	71	6	table	table	NOUN
hjic-1020	71	7	1	1	NUM
hjic-1020	71	8	represents	represent	VERB
hjic-1020	71	9	the	the	DET
hjic-1020	71	10	corresponding	corresponding	ADJ
hjic-1020	71	11	average	average	ADJ
hjic-1020	71	12	values	value	NOUN
hjic-1020	71	13	.	.	PUNCT
hjic-1020	72	1	in	in	ADP
hjic-1020	72	2	all	all	DET
hjic-1020	72	3	tests	test	NOUN
hjic-1020	72	4	,	,	PUNCT
hjic-1020	72	5	for	for	ADP
hjic-1020	72	6	a	a	DET
hjic-1020	72	7	correct	correct	ADJ
hjic-1020	72	8	comparison	comparison	NOUN
hjic-1020	72	9	of	of	ADP
hjic-1020	72	10	the	the	DET
hjic-1020	72	11	results	result	NOUN
hjic-1020	72	12	,	,	PUNCT
hjic-1020	72	13	the	the	DET
hjic-1020	72	14	total	total	ADJ
hjic-1020	72	15	number	number	NOUN
hjic-1020	72	16	of	of	ADP
hjic-1020	72	17	generations	generation	NOUN
hjic-1020	72	18	(	(	PUNCT
hjic-1020	72	19	the	the	DET
hjic-1020	72	20	stopping	stopping	NOUN
hjic-1020	72	21	criterion	criterion	NOUN
hjic-1020	72	22	)	)	PUNCT
hjic-1020	72	23	was	be	AUX
hjic-1020	72	24	the	the	DET
hjic-1020	72	25	same	same	ADJ
hjic-1020	72	26	,	,	PUNCT
hjic-1020	72	27	respectively	respectively	ADV
hjic-1020	72	28	1000	1000	NUM
hjic-1020	72	29	.	.	PUNCT
hjic-1020	73	1	fig	fig	NOUN
hjic-1020	73	2	.	.	PUNCT
hjic-1020	73	3	]	]	PUNCT
hjic-1020	74	1	the	the	DET
hjic-1020	74	2	evolution	evolution	NOUN
hjic-1020	74	3	of	of	ADP
hjic-1020	74	4	mean	mean	PROPN
hjic-1020	74	5	(	(	PUNCT
hjic-1020	74	6	....	....	PUNCT
hjic-1020	74	7	)	)	PUNCT
hjic-1020	74	8	and	and	CCONJ
hjic-1020	74	9	best	good	ADJ
hjic-1020	74	10	(	(	PUNCT
hjic-1020	74	11	-)fitness	-)fitness	PUNCT
hjic-1020	74	12	for	for	ADP
hjic-1020	74	13	the	the	DET
hjic-1020	74	14	optimum	optimum	ADJ
hjic-1020	74	15	neural	neural	ADJ
hjic-1020	74	16	network	network	NOUN
hjic-1020	74	17	the	the	DET
hjic-1020	74	18	analysis	analysis	NOUN
hjic-1020	74	19	of	of	ADP
hjic-1020	74	20	these	these	DET
hjic-1020	74	21	results	result	NOUN
hjic-1020	74	22	indicates	indicate	VERB
hjic-1020	74	23	that	that	SCONJ
hjic-1020	74	24	:	:	PUNCT
hjic-1020	74	25	expansion	expansion	NOUN
hjic-1020	74	26	of	of	ADP
hjic-1020	74	27	the	the	DET
hjic-1020	74	28	initial	initial	ADJ
hjic-1020	74	29	population	population	NOUN
hjic-1020	74	30	size	size	NOUN
hjic-1020	74	31	has	have	VERB
hjic-1020	74	32	a	a	DET
hjic-1020	74	33	favorable	favorable	ADJ
hjic-1020	74	34	effect	effect	NOUN
hjic-1020	74	35	due	due	ADP
hjic-1020	74	36	to	to	ADP
hjic-1020	74	37	increasing	increase	VERB
hjic-1020	74	38	the	the	DET
hjic-1020	74	39	probability	probability	NOUN
hjic-1020	74	40	to	to	PART
hjic-1020	74	41	generate	generate	VERB
hjic-1020	74	42	individuals	individual	NOUN
hjic-1020	74	43	with	with	ADP
hjic-1020	74	44	a	a	DET
hjic-1020	74	45	good	good	ADJ
hjic-1020	74	46	fitness	fitness	NOUN
hjic-1020	74	47	.	.	PUNCT
hjic-1020	75	1	unfortunately	unfortunately	ADV
hjic-1020	75	2	,	,	PUNCT
hjic-1020	75	3	this	this	DET
hjic-1020	75	4	effect	effect	NOUN
hjic-1020	75	5	is	be	AUX
hjic-1020	75	6	present	present	ADJ
hjic-1020	75	7	only	only	ADV
hjic-1020	75	8	in	in	ADP
hjic-1020	75	9	simple	simple	ADJ
hjic-1020	75	10	cases	case	NOUN
hjic-1020	75	11	,	,	PUNCT
hjic-1020	75	12	for	for	ADP
hjic-1020	75	13	well	well	ADV
hjic-1020	75	14	-	-	PUNCT
hjic-1020	75	15	defined	define	VERB
hjic-1020	75	16	problems	problem	NOUN
hjic-1020	75	17	with	with	ADP
hjic-1020	75	18	smooth	smooth	ADJ
hjic-1020	75	19	response	response	NOUN
hjic-1020	75	20	surface	surface	NOUN
hjic-1020	75	21	.	.	PUNCT
hjic-1020	76	1	for	for	ADP
hjic-1020	76	2	highly	highly	ADV
hjic-1020	76	3	complex	complex	ADJ
hjic-1020	76	4	.	.	PUNCT
hjic-1020	77	1	problems	problem	NOUN
hjic-1020	77	2	,	,	PUNCT
hjic-1020	77	3	the	the	DET
hjic-1020	77	4	search	search	NOUN
hjic-1020	77	5	space	space	NOUN
hjic-1020	77	6	in	in	ADP
hjic-1020	77	7	which	which	PRON
hjic-1020	77	8	genetic	genetic	ADJ
hjic-1020	77	9	algorithm	algorithm	NOUN
hjic-1020	77	10	usually	usually	ADV
hjic-1020	77	11	operates	operate	VERB
hjic-1020	77	12	is	be	AUX
hjic-1020	77	13	so	so	ADV
hjic-1020	77	14	large	large	ADJ
hjic-1020	77	15	,	,	PUNCT
hjic-1020	77	16	that	that	SCONJ
hjic-1020	77	17	taking	take	VERB
hjic-1020	77	18	few	few	ADJ
hjic-1020	77	19	additional	additional	ADJ
hjic-1020	77	20	chromosomes	chromosome	NOUN
hjic-1020	77	21	into	into	ADP
hjic-1020	77	22	initial	initial	ADJ
hjic-1020	77	23	population	population	NOUN
hjic-1020	77	24	cause	cause	VERB
hjic-1020	77	25	a	a	DET
hjic-1020	77	26	very	very	ADV
hjic-1020	77	27	small	small	ADJ
hjic-1020	77	28	,	,	PUNCT
hjic-1020	77	29	or	or	CCONJ
hjic-1020	77	30	no	no	DET
hjic-1020	77	31	detectable	detectable	ADJ
hjic-1020	77	32	effect	effect	NOUN
hjic-1020	77	33	,	,	PUNCT
hjic-1020	77	34	unless	unless	SCONJ
hjic-1020	77	35	the	the	DET
hjic-1020	77	36	problem	problem	NOUN
hjic-1020	77	37	surface	surface	NOUN
hjic-1020	77	38	is	be	AUX
hjic-1020	77	39	very	very	ADV
hjic-1020	77	40	smooth	smooth	ADJ
hjic-1020	77	41	.	.	PUNCT
hjic-1020	78	1	a	a	DET
hjic-1020	78	2	too	too	ADV
hjic-1020	78	3	small	small	ADJ
hjic-1020	78	4	,	,	PUNCT
hjic-1020	78	5	or	or	CCONJ
hjic-1020	78	6	a	a	DET
hjic-1020	78	7	too	too	ADV
hjic-1020	78	8	large	large	ADJ
hjic-1020	78	9	range	range	NOUN
hjic-1020	78	10	of	of	ADP
hjic-1020	78	11	the	the	DET
hjic-1020	78	12	weights	weight	NOUN
hjic-1020	78	13	values	value	NOUN
hjic-1020	78	14	has	have	VERB
hjic-1020	78	15	a	a	DET
hjic-1020	78	16	non	non	ADJ
hjic-1020	78	17	-	-	ADJ
hjic-1020	78	18	favorable	favorable	ADJ
hjic-1020	78	19	effect	effect	NOUN
hjic-1020	78	20	:	:	PUNCT
hjic-1020	78	21	in	in	ADP
hjic-1020	78	22	the	the	DET
hjic-1020	78	23	first	first	ADJ
hjic-1020	78	24	situation	situation	NOUN
hjic-1020	78	25	the	the	DET
hjic-1020	78	26	optimization	optimization	NOUN
hjic-1020	78	27	solution	solution	NOUN
hjic-1020	78	28	is	be	AUX
hjic-1020	78	29	arbitrarily	arbitrarily	ADV
hjic-1020	78	30	restricted	restrict	VERB
hjic-1020	78	31	,	,	PUNCT
hjic-1020	78	32	and	and	CCONJ
hjic-1020	78	33	in	in	ADP
hjic-1020	78	34	the	the	DET
hjic-1020	78	35	second	second	ADJ
hjic-1020	78	36	case	case	NOUN
hjic-1020	78	37	the	the	DET
hjic-1020	78	38	search	search	NOUN
hjic-1020	78	39	space	space	NOUN
hjic-1020	78	40	is	be	AUX
hjic-1020	78	41	unreasonably	unreasonably	ADV
hjic-1020	78	42	enlarged	enlarge	VERB
hjic-1020	78	43	.	.	PUNCT
hjic-1020	79	1	as	as	ADP
hjic-1020	79	2	in	in	ADP
hjic-1020	79	3	the	the	DET
hjic-1020	79	4	case	case	NOUN
hjic-1020	79	5	of	of	ADP
hjic-1020	79	6	gradient	gradient	NOUN
hjic-1020	79	7	-	-	PUNCT
hjic-1020	79	8	based	base	VERB
hjic-1020	79	9	learning	learning	NOUN
hjic-1020	79	10	algorithms	algorithm	NOUN
hjic-1020	79	11	,	,	PUNCT
hjic-1020	79	12	there	there	PRON
hjic-1020	79	13	is	be	VERB
hjic-1020	79	14	an	an	DET
hjic-1020	79	15	optimum	optimum	ADJ
hjic-1020	79	16	number	number	NOUN
hjic-1020	79	17	of	of	ADP
hjic-1020	79	18	neurons	neuron	NOUN
hjic-1020	79	19	in	in	ADP
hjic-1020	79	20	the	the	DET
hjic-1020	79	21	hidden	hide	VERB
hjic-1020	79	22	layer	layer	NOUN
hjic-1020	79	23	for	for	ADP
hjic-1020	79	24	each	each	DET
hjic-1020	79	25	neural	neural	ADJ
hjic-1020	79	26	network	network	NOUN
hjic-1020	79	27	application	application	NOUN
hjic-1020	79	28	,	,	PUNCT
hjic-1020	79	29	and	and	CCONJ
hjic-1020	79	30	the	the	DET
hjic-1020	79	31	corresponding	corresponding	ADJ
hjic-1020	79	32	value	value	NOUN
hjic-1020	79	33	must	must	AUX
hjic-1020	79	34	be	be	AUX
hjic-1020	79	35	establish	establish	VERB
hjic-1020	79	36	through	through	ADP
hjic-1020	79	37	an	an	DET
hjic-1020	79	38	iterative	iterative	ADJ
hjic-1020	79	39	procedure	procedure	NOUN
hjic-1020	79	40	.	.	PUNCT
hjic-1020	80	1	similarly	similarly	ADV
hjic-1020	80	2	,	,	PUNCT
hjic-1020	80	3	the	the	DET
hjic-1020	80	4	best	good	ADJ
hjic-1020	80	5	rule	rule	NOUN
hjic-1020	80	6	of	of	ADP
hjic-1020	80	7	classes	class	NOUN
hjic-1020	80	8	codification	codification	NOUN
hjic-1020	80	9	and	and	CCONJ
hjic-1020	80	10	the	the	DET
hjic-1020	80	11	corresponding	corresponding	ADJ
hjic-1020	80	12	number	number	NOUN
hjic-1020	80	13	of	of	ADP
hjic-1020	80	14	neurons	neuron	NOUN
hjic-1020	80	15	in	in	ADP
hjic-1020	80	16	the	the	DET
hjic-1020	80	17	output	output	NOUN
hjic-1020	80	18	layer	layer	NOUN
hjic-1020	80	19	must	must	AUX
hjic-1020	80	20	be	be	AUX
hjic-1020	80	21	found	find	VERB
hjic-1020	80	22	by	by	ADP
hjic-1020	80	23	iterations	iteration	NOUN
hjic-1020	80	24	.	.	PUNCT
hjic-1020	81	1	with	with	ADP
hjic-1020	81	2	the	the	DET
hjic-1020	81	3	best	good	ADJ
hjic-1020	81	4	values	value	NOUN
hjic-1020	81	5	of	of	ADP
hjic-1020	81	6	the	the	DET
hjic-1020	81	7	investigated	investigate	VERB
hjic-1020	81	8	factors	factor	NOUN
hjic-1020	81	9	from	from	ADP
hjic-1020	81	10	table	table	NOUN
hjic-1020	81	11	1	1	NUM
hjic-1020	81	12	,	,	PUNCT
hjic-1020	81	13	respectively	respectively	ADV
hjic-1020	81	14	sip=	sip=	PROPN
hjic-1020	81	15	500	500	NUM
hjic-1020	81	16	;	;	PUNCT
hjic-1020	81	17	b=	b=	NOUN
hjic-1020	82	1	[	[	X
hjic-1020	82	2	-100	-100	X
hjic-1020	82	3	;	;	PUNCT
hjic-1020	82	4	100	100	NUM
hjic-1020	82	5	]	]	PUNCT
hjic-1020	82	6	;	;	PUNCT
hjic-1020	82	7	nh=	nh=	PROPN
hjic-1020	82	8	1	1	NUM
hjic-1020	82	9	;	;	PUNCT
hjic-1020	82	10	no=	no=	PROPN
hjic-1020	82	11	3	3	NUM
hjic-1020	82	12	,	,	PUNCT
hjic-1020	82	13	and	and	CCONJ
hjic-1020	82	14	by	by	ADP
hjic-1020	82	15	the	the	DET
hjic-1020	82	16	aid	aid	NOUN
hjic-1020	82	17	of	of	ADP
hjic-1020	82	18	the	the	DET
hjic-1020	82	19	genetic	genetic	ADJ
hjic-1020	82	20	algorithm	algorithm	NOUN
hjic-1020	82	21	the	the	DET
hjic-1020	82	22	weights	weight	NOUN
hjic-1020	82	23	of	of	ADP
hjic-1020	82	24	the	the	DET
hjic-1020	82	25	corresponding	corresponding	ADJ
hjic-1020	82	26	neural	neural	ADJ
hjic-1020	82	27	network	network	NOUN
hjic-1020	82	28	were	be	AUX
hjic-1020	82	29	established	establish	VERB
hjic-1020	82	30	.	.	PUNCT
hjic-1020	83	1	this	this	PRON
hjic-1020	83	2	exhibit	exhibit	VERB
hjic-1020	83	3	excellent	excellent	ADJ
hjic-1020	83	4	results	result	NOUN
hjic-1020	83	5	:	:	PUNCT
hjic-1020	83	6	rms	rm	NOUN
hjic-1020	83	7	error	error	NOUN
hjic-1020	83	8	=	=	SYM
hjic-1020	83	9	0.007	0.007	NUM
hjic-1020	83	10	and	and	CCONJ
hjic-1020	83	11	no	no	DET
hjic-1020	83	12	wrong	wrong	ADJ
hjic-1020	83	13	classification	classification	NOUN
hjic-1020	83	14	(	(	PUNCT
hjic-1020	83	15	related	relate	VERB
hjic-1020	83	16	to	to	ADP
hjic-1020	83	17	the	the	DET
hjic-1020	83	18	training	training	NOUN
hjic-1020	83	19	set	set	NOUN
hjic-1020	83	20	,	,	PUNCT
hjic-1020	83	21	due	due	ADP
hjic-1020	83	22	to	to	ADP
hjic-1020	83	23	the	the	DET
hjic-1020	83	24	absence	absence	NOUN
hjic-1020	83	25	in	in	ADP
hjic-1020	83	26	the	the	DET
hjic-1020	83	27	original	original	ADJ
hjic-1020	83	28	mentioned	mention	VERB
hjic-1020	83	29	reference	reference	NOUN
hjic-1020	84	1	[	[	X
hjic-1020	84	2	9	9	NUM
hjic-1020	84	3	]	]	PUNCT
hjic-1020	84	4	of	of	ADP
hjic-1020	84	5	a	a	DET
hjic-1020	84	6	test	test	NOUN
hjic-1020	84	7	set	set	NOUN
hjic-1020	84	8	)	)	PUNCT
hjic-1020	84	9	.	.	PUNCT
hjic-1020	85	1	the	the	DET
hjic-1020	85	2	corresponding	corresponding	ADJ
hjic-1020	85	3	evolution	evolution	NOUN
hjic-1020	85	4	of	of	ADP
hjic-1020	85	5	the	the	DET
hjic-1020	85	6	genetic	genetic	ADJ
hjic-1020	85	7	algorithm	algorithm	NOUN
hjic-1020	85	8	is	be	AUX
hjic-1020	85	9	represented	represent	VERB
hjic-1020	85	10	in	in	ADP
hjic-1020	85	11	fig.l	fig.l	PROPN
hjic-1020	85	12	.	.	PUNCT
hjic-1020	86	1	it	it	PRON
hjic-1020	86	2	is	be	AUX
hjic-1020	86	3	remarkable	remarkable	ADJ
hjic-1020	86	4	that	that	SCONJ
hjic-1020	86	5	the	the	DET
hjic-1020	86	6	best	good	ADJ
hjic-1020	86	7	values	value	NOUN
hjic-1020	86	8	of	of	ADP
hjic-1020	86	9	rms	rm	NOUN
hjic-1020	86	10	error	error	NOUN
hjic-1020	86	11	obtained	obtain	VERB
hjic-1020	86	12	for	for	ADP
hjic-1020	86	13	this	this	DET
hjic-1020	86	14	example	example	NOUN
hjic-1020	86	15	by	by	ADP
hjic-1020	86	16	baughman	baughman	NOUN
hjic-1020	86	17	and	and	CCONJ
hjic-1020	86	18	liu	liu	PROPN
hjic-1020	87	1	[	[	X
hjic-1020	87	2	9	9	NUM
hjic-1020	87	3	]	]	PUNCT
hjic-1020	87	4	(	(	PUNCT
hjic-1020	87	5	using	use	VERB
hjic-1020	87	6	the	the	DET
hjic-1020	87	7	classical	classical	ADJ
hjic-1020	87	8	"	"	PUNCT
hjic-1020	87	9	delta	delta	NOUN
hjic-1020	87	10	rule	rule	NOUN
hjic-1020	87	11	"	"	PUNCT
hjic-1020	87	12	as	as	ADP
hjic-1020	87	13	learning	learn	VERB
hjic-1020	87	14	procedure	procedure	NOUN
hjic-1020	87	15	)	)	PUNCT
hjic-1020	87	16	at	at	ADP
hjic-1020	87	17	the	the	DET
hjic-1020	87	18	end	end	NOUN
hjic-1020	87	19	of	of	ADP
hjic-1020	87	20	50,000	50,000	NUM
hjic-1020	87	21	training	training	NOUN
hjic-1020	87	22	iterations	iteration	NOUN
hjic-1020	87	23	were	be	AUX
hjic-1020	87	24	:	:	PUNCT
hjic-1020	87	25	0.088	0.088	NUM
hjic-1020	87	26	for	for	ADP
hjic-1020	87	27	a	a	DET
hjic-1020	87	28	network	network	NOUN
hjic-1020	87	29	with	with	ADP
hjic-1020	87	30	3	3	NUM
hjic-1020	87	31	neurons	neuron	NOUN
hjic-1020	87	32	in	in	ADP
hjic-1020	87	33	the	the	DET
hjic-1020	87	34	·	·	PUNCT
hjic-1020	87	35	hidden	hidden	ADJ
hjic-1020	87	36	layer	layer	NOUN
hjic-1020	87	37	and	and	CCONJ
hjic-1020	87	38	sigmoid	sigmoid	NOUN
hjic-1020	87	39	transfer	transfer	NOUN
hjic-1020	87	40	function	function	NOUN
hjic-1020	87	41	,	,	PUNCT
hjic-1020	87	42	and	and	CCONJ
hjic-1020	87	43	0.037	0.037	NUM
hjic-1020	87	44	for	for	ADP
hjic-1020	87	45	a	a	DET
hjic-1020	87	46	network	network	NOUN
hjic-1020	87	47	with	with	ADP
hjic-1020	87	48	5	5	NUM
hjic-1020	87	49	243	243	NUM
hjic-1020	87	50	neurons	neuron	NOUN
hjic-1020	87	51	in	in	ADP
hjic-1020	87	52	the	the	DET
hjic-1020	87	53	hidden	hide	VERB
hjic-1020	87	54	layer	layer	NOUN
hjic-1020	87	55	and	and	CCONJ
hjic-1020	87	56	hyperbolic	hyperbolic	ADJ
hjic-1020	87	57	tangent	tangent	NOUN
hjic-1020	87	58	transfer	transfer	NOUN
hjic-1020	87	59	function	function	NOUN
hjic-1020	87	60	.	.	PUNCT
hjic-1020	88	1	consistency	consistency	NOUN
hjic-1020	88	2	analysis	analysis	NOUN
hjic-1020	88	3	of	of	ADP
hjic-1020	88	4	fuzzy	fuzzy	ADJ
hjic-1020	88	5	sets	set	NOUN
hjic-1020	88	6	this	this	DET
hjic-1020	88	7	case	case	NOUN
hjic-1020	88	8	study	study	NOUN
hjic-1020	88	9	was	be	AUX
hjic-1020	88	10	already	already	ADV
hjic-1020	88	11	presented	present	VERB
hjic-1020	88	12	in	in	ADP
hjic-1020	88	13	literature	literature	NOUN
hjic-1020	88	14	[	[	X
hjic-1020	88	15	10	10	NUM
hjic-1020	88	16	]	]	PUNCT
hjic-1020	88	17	as	as	ADP
hjic-1020	88	18	an	an	DET
hjic-1020	88	19	application	application	NOUN
hjic-1020	88	20	of	of	ADP
hjic-1020	88	21	a	a	DET
hjic-1020	88	22	modified	modified	ADJ
hjic-1020	88	23	back	back	ADJ
hjic-1020	88	24	-	-	PUNCT
hjic-1020	88	25	propagation	propagation	NOUN
hjic-1020	88	26	neural	neural	ADJ
hjic-1020	88	27	network	network	NOUN
hjic-1020	88	28	.	.	PUNCT
hjic-1020	89	1	the	the	DET
hjic-1020	89	2	reason	reason	NOUN
hjic-1020	89	3	of	of	ADP
hjic-1020	89	4	the	the	DET
hjic-1020	89	5	actual	actual	ADJ
hjic-1020	89	6	selection	selection	NOUN
hjic-1020	89	7	consists	consist	VERB
hjic-1020	89	8	in	in	ADP
hjic-1020	89	9	the	the	DET
hjic-1020	89	10	fact	fact	NOUN
hjic-1020	89	11	that	that	SCONJ
hjic-1020	89	12	the	the	DET
hjic-1020	89	13	·	·	PUNCT
hjic-1020	89	14	application	application	NOUN
hjic-1020	89	15	represents	represent	VERB
hjic-1020	89	16	a	a	DET
hjic-1020	89	17	difficult	difficult	ADJ
hjic-1020	89	18	classification	classification	NOUN
hjic-1020	89	19	problem	problem	NOUN
hjic-1020	89	20	·	·	PUNCT
hjic-1020	89	21	involving	involve	VERB
hjic-1020	89	22	non	non	ADJ
hjic-1020	89	23	-	-	ADJ
hjic-1020	89	24	linearly	linearly	ADV
hjic-1020	89	25	separable	separable	ADJ
hjic-1020	89	26	patterns	pattern	NOUN
hjic-1020	89	27	.	.	PUNCT
hjic-1020	90	1	in	in	ADP
hjic-1020	90	2	many	many	ADJ
hjic-1020	90	3	chemical	chemical	NOUN
hjic-1020	90	4	processes	process	NOUN
hjic-1020	90	5	due	due	ADJ
hjic-1020	90	6	to	to	ADP
hjic-1020	90	7	several	several	ADJ
hjic-1020	90	8	reasons	reason	NOUN
hjic-1020	90	9	there	there	PRON
hjic-1020	90	10	are	be	VERB
hjic-1020	90	11	some	some	DET
hjic-1020	90	12	variables	variable	NOUN
hjic-1020	90	13	that	that	PRON
hjic-1020	90	14	can	can	AUX
hjic-1020	90	15	not	not	PART
hjic-1020	90	16	be	be	AUX
hjic-1020	90	17	measured	measure	VERB
hjic-1020	90	18	directly	directly	ADV
hjic-1020	90	19	on	on	ADP
hjic-1020	90	20	-	-	PUNCT
hjic-1020	90	21	line	line	NOUN
hjic-1020	90	22	(	(	PUNCT
hjic-1020	90	23	e.g.	e.g.	ADV
hjic-1020	90	24	biological	biological	ADJ
hjic-1020	90	25	or	or	CCONJ
hjic-1020	90	26	catalyst	catalyst	NOUN
hjic-1020	90	27	activity	activity	NOUN
hjic-1020	90	28	)	)	PUNCT
hjic-1020	90	29	.	.	PUNCT
hjic-1020	91	1	in	in	ADP
hjic-1020	91	2	these	these	DET
hjic-1020	91	3	cases	case	NOUN
hjic-1020	91	4	the	the	DET
hjic-1020	91	5	values	value	NOUN
hjic-1020	91	6	of	of	ADP
hjic-1020	91	7	measured	measured	ADJ
hjic-1020	91	8	variables	variable	NOUN
hjic-1020	91	9	are	be	AUX
hjic-1020	91	10	fuzzy	fuzzy	ADJ
hjic-1020	91	11	sets	set	NOUN
hjic-1020	91	12	.	.	PUNCT
hjic-1020	92	1	related	relate	VERB
hjic-1020	92	2	to	to	ADP
hjic-1020	92	3	system	system	NOUN
hjic-1020	92	4	constraints	constraint	NOUN
hjic-1020	92	5	and	and	CCONJ
hjic-1020	92	6	feasible	feasible	ADJ
hjic-1020	92	7	domains	domain	NOUN
hjic-1020	92	8	of	of	ADP
hjic-1020	92	9	the	the	DET
hjic-1020	92	10	variables	variable	NOUN
hjic-1020	92	11	these	these	DET
hjic-1020	92	12	fuzzy	fuzzy	ADJ
hjic-1020	92	13	sets	set	NOUN
hjic-1020	92	14	must	must	AUX
hjic-1020	92	15	be	be	AUX
hjic-1020	92	16	consistent	consistent	ADJ
hjic-1020	92	17	.	.	PUNCT
hjic-1020	93	1	in	in	ADP
hjic-1020	93	2	order	order	NOUN
hjic-1020	93	3	to	to	PART
hjic-1020	93	4	use	use	VERB
hjic-1020	93	5	a	a	DET
hjic-1020	93	6	graphic	graphic	ADJ
hjic-1020	93	7	representation	representation	NOUN
hjic-1020	93	8	,	,	PUNCT
hjic-1020	93	9	a	a	DET
hjic-1020	93	10	hypothetical	hypothetical	ADJ
hjic-1020	93	11	example	example	NOUN
hjic-1020	93	12	with	with	ADP
hjic-1020	93	13	two	two	NUM
hjic-1020	93	14	measured	measured	ADJ
hjic-1020	93	15	variables	variable	NOUN
hjic-1020	93	16	,	,	PUNCT
hjic-1020	93	17	x1	x1	PROPN
hjic-1020	93	18	and	and	CCONJ
hjic-1020	93	19	x2	x2	NUM
hjic-1020	93	20	,	,	PUNCT
hjic-1020	93	21	and	and	CCONJ
hjic-1020	93	22	one	one	NUM
hjic-1020	93	23	unmeasured	unmeasured	ADJ
hjic-1020	93	24	variable	variable	NOUN
hjic-1020	93	25	,	,	PUNCT
hjic-1020	93	26	x3	x3	ADJ
hjic-1020	93	27	,	,	PUNCT
hjic-1020	93	28	all	all	PRON
hjic-1020	93	29	restricted	restrict	VERB
hjic-1020	93	30	to	to	ADP
hjic-1020	93	31	a	a	DET
hjic-1020	93	32	system	system	NOUN
hjic-1020	93	33	of	of	ADP
hjic-1020	93	34	three	three	NUM
hjic-1020	93	35	strongly	strongly	ADV
hjic-1020	93	36	non	non	ADJ
hjic-1020	93	37	-	-	ADJ
hjic-1020	93	38	linear	linear	ADJ
hjic-1020	93	39	constrains	constrain	NOUN
hjic-1020	93	40	,	,	PUNCT
hjic-1020	93	41	was	be	AUX
hjic-1020	93	42	considered	consider	VERB
hjic-1020	93	43	:	:	PUNCT
hjic-1020	93	44	o.sx3	o.sx3	PART
hjic-1020	93	45	+	+	NOUN
hjic-1020	93	46	0.275x1x3	0.275x1x3	X
hjic-1020	93	47	+	+	ADJ
hjic-1020	93	48	0.261x	0.261x	ADJ
hjic-1020	93	49	;	;	PUNCT
hjic-1020	93	50	x3	x3	PROPN
hjic-1020	93	51	+	+	NOUN
hjic-1020	93	52	0.18xt	0.18xt	NOUN
hjic-1020	93	53	x3+x2=0	x3+x2=0	X
hjic-1020	93	54	(	(	PUNCT
hjic-1020	93	55	3	3	X
hjic-1020	93	56	)	)	PUNCT
hjic-1020	93	57	the	the	DET
hjic-1020	93	58	feasible	feasible	ADJ
hjic-1020	93	59	domains	domain	NOUN
hjic-1020	93	60	of	of	ADP
hjic-1020	93	61	the	the	DET
hjic-1020	93	62	variables	variable	NOUN
hjic-1020	93	63	are	be	AUX
hjic-1020	93	64	:	:	PUNCT
hjic-1020	93	65	the	the	DET
hjic-1020	93	66	number	number	NOUN
hjic-1020	93	67	of	of	ADP
hjic-1020	93	68	variables	variable	NOUN
hjic-1020	93	69	is	be	AUX
hjic-1020	93	70	incidentally	incidentally	ADV
hjic-1020	93	71	~e	~e	NUM
hjic-1020	93	72	same	same	ADJ
hjic-1020	93	73	as	as	ADP
hjic-1020	93	74	the	the	DET
hjic-1020	93	75	number	number	NOUN
hjic-1020	93	76	of	of	ADP
hjic-1020	93	77	constraints	constraint	NOUN
hjic-1020	93	78	.	.	PUNCT
hjic-1020	94	1	usually	usually	ADV
hjic-1020	94	2	,	,	PUNCT
hjic-1020	94	3	the	the	DET
hjic-1020	94	4	number	number	NOUN
hjic-1020	94	5	of	of	ADP
hjic-1020	94	6	variables	variable	NOUN
hjic-1020	94	7	is	be	AUX
hjic-1020	94	8	greater	great	ADJ
hjic-1020	94	9	than	than	ADP
hjic-1020	94	10	the	the	DET
hjic-1020	94	11	number	number	NOUN
hjic-1020	94	12	of	of	ADP
hjic-1020	94	13	constraints	constraint	NOUN
hjic-1020	94	14	.	.	PUNCT
hjic-1020	95	1	the	the	DET
hjic-1020	95	2	measured	measured	ADJ
hjic-1020	95	3	values	value	NOUN
hjic-1020	95	4	of	of	ADP
hjic-1020	95	5	the	the	DET
hjic-1020	95	6	variables	variable	NOUN
hjic-1020	95	7	correlated	correlate	VERB
hjic-1020	95	8	with	with	ADP
hjic-1020	95	9	the	the	DET
hjic-1020	95	10	feasible	feasible	ADJ
hjic-1020	95	11	domains	domain	NOUN
hjic-1020	95	12	of	of	ADP
hjic-1020	95	13	all	all	DET
hjic-1020	95	14	variables	variable	NOUN
hjic-1020	95	15	,	,	PUNCT
hjic-1020	95	16	and	and	CCONJ
hjic-1020	95	17	with	with	ADP
hjic-1020	95	18	constraints	constraint	NOUN
hjic-1020	95	19	'	'	PART
hjic-1020	95	20	violations	violation	NOUN
hjic-1020	95	21	will	will	AUX
hjic-1020	95	22	generate	generate	VERB
hjic-1020	95	23	several	several	ADJ
hjic-1020	95	24	classes	class	NOUN
hjic-1020	95	25	of	of	ADP
hjic-1020	95	26	errors	error	NOUN
hjic-1020	95	27	,	,	PUNCT
hjic-1020	95	28	depending	depend	VERB
hjic-1020	95	29	on	on	ADP
hjic-1020	95	30	combination	combination	NOUN
hjic-1020	95	31	of	of	ADP
hjic-1020	95	32	violated	violate	VERB
hjic-1020	95	33	restrictions	restriction	NOUN
hjic-1020	95	34	.	.	PUNCT
hjic-1020	96	1	for	for	ADP
hjic-1020	96	2	this	this	DET
hjic-1020	96	3	application	application	NOUN
hjic-1020	96	4	the	the	DET
hjic-1020	96	5	following	follow	VERB
hjic-1020	96	6	four	four	NUM
hjic-1020	96	7	classes	class	NOUN
hjic-1020	96	8	were	be	AUX
hjic-1020	96	9	considered	consider	VERB
hjic-1020	96	10	:	:	PUNCT
hjic-1020	96	11	a	a	DET
hjic-1020	96	12	-all	-all	PUNCT
hjic-1020	96	13	restrictions	restriction	NOUN
hjic-1020	96	14	are	be	AUX
hjic-1020	96	15	satisfied	satisfied	ADJ
hjic-1020	96	16	;	;	PUNCT
hjic-1020	96	17	b	b	X
hjic-1020	96	18	-constraint	-constraint	NOUN
hjic-1020	96	19	(	(	PUNCT
hjic-1020	96	20	1	1	NUM
hjic-1020	96	21	)	)	PUNCT
hjic-1020	96	22	is	be	AUX
hjic-1020	96	23	violated	violate	VERB
hjic-1020	96	24	regardless	regardless	ADV
hjic-1020	96	25	of	of	ADP
hjic-1020	96	26	constraint	constraint	NOUN
hjic-1020	96	27	(	(	PUNCT
hjic-1020	96	28	2	2	NUM
hjic-1020	96	29	)	)	PUNCT
hjic-1020	96	30	and	and	CCONJ
hjic-1020	96	31	constraint	constraint	NOUN
hjic-1020	96	32	(	(	PUNCT
hjic-1020	96	33	3	3	NUM
hjic-1020	96	34	)	)	PUNCT
hjic-1020	96	35	;	;	PUNCT
hjic-1020	96	36	cconstraint	cconstraint	NOUN
hjic-1020	96	37	(	(	PUNCT
hjic-1020	96	38	2	2	X
hjic-1020	96	39	)	)	PUNCT
hjic-1020	96	40	is	be	AUX
hjic-1020	96	41	violated	violate	VERB
hjic-1020	96	42	and	and	CCONJ
hjic-1020	96	43	constraint	constraint	NOUN
hjic-1020	96	44	(	(	PUNCT
hjic-1020	96	45	1	1	NUM
hjic-1020	96	46	)	)	PUNCT
hjic-1020	96	47	and	and	CCONJ
hjic-1020	96	48	constraint	constraint	NOUN
hjic-1020	96	49	(	(	PUNCT
hjic-1020	96	50	3	3	X
hjic-1020	96	51	)	)	PUNCT
hjic-1020	96	52	are	be	AUX
hjic-1020	96	53	respected	respect	VERB
hjic-1020	96	54	;	;	PUNCT
hjic-1020	96	55	d	d	X
hjic-1020	96	56	-constraint	-constraint	NOUN
hjic-1020	96	57	(	(	PUNCT
hjic-1020	96	58	3	3	NUM
hjic-1020	96	59	)	)	PUNCT
hjic-1020	96	60	is	be	AUX
hjic-1020	96	61	violated	violate	VERB
hjic-1020	96	62	regardless	regardless	ADV
hjic-1020	96	63	of	of	ADP
hjic-1020	96	64	constraint	constraint	NOUN
hjic-1020	96	65	(	(	PUNCT
hjic-1020	96	66	1	1	NUM
hjic-1020	96	67	)	)	PUNCT
hjic-1020	96	68	and	and	CCONJ
hjic-1020	96	69	constraint	constraint	NOUN
hjic-1020	96	70	(	(	PUNCT
hjic-1020	96	71	2	2	NUM
hjic-1020	96	72	)	)	PUNCT
hjic-1020	96	73	.	.	PUNCT
hjic-1020	97	1	these	these	DET
hjic-1020	97	2	four	four	NUM
hjic-1020	97	3	domains	domain	NOUN
hjic-1020	97	4	,	,	PUNCT
hjic-1020	97	5	the	the	DET
hjic-1020	97	6	data	datum	NOUN
hjic-1020	97	7	from	from	ADP
hjic-1020	97	8	the	the	DET
hjic-1020	97	9	training	training	NOUN
hjic-1020	97	10	set	set	NOUN
hjic-1020	97	11	(	(	PUNCT
hjic-1020	97	12	points	point	NOUN
hjic-1020	97	13	numbered	number	VERB
hjic-1020	97	14	from	from	ADP
hjic-1020	97	15	1	1	NUM
hjic-1020	97	16	to	to	ADP
hjic-1020	97	17	124	124	NUM
hjic-1020	97	18	)	)	PUNCT
hjic-1020	97	19	,	,	PUNCT
hjic-1020	97	20	and	and	CCONJ
hjic-1020	97	21	test	test	NOUN
hjic-1020	97	22	data	datum	NOUN
hjic-1020	97	23	(	(	PUNCT
hjic-1020	97	24	prefix	prefix	NOUN
hjic-1020	97	25	t	t	PROPN
hjic-1020	97	26	)	)	PUNCT
hjic-1020	97	27	are	be	AUX
hjic-1020	97	28	indicated	indicate	VERB
hjic-1020	97	29	in	in	ADP
hjic-1020	97	30	fig.2	fig.2	PROPN
hjic-1020	97	31	.	.	PUNCT
hjic-1020	98	1	it	it	PRON
hjic-1020	98	2	can	can	AUX
hjic-1020	98	3	be	be	AUX
hjic-1020	98	4	observed	observe	VERB
hjic-1020	98	5	that	that	SCONJ
hjic-1020	98	6	all	all	PRON
hjic-1020	98	7	learning	learn	VERB
hjic-1020	98	8	data	datum	NOUN
hjic-1020	98	9	correspond	correspond	VERB
hjic-1020	98	10	to	to	ADP
hjic-1020	98	11	points	point	NOUN
hjic-1020	98	12	placed	place	VERB
hjic-1020	98	13	on	on	ADP
hjic-1020	98	14	.	.	PUNCT
hjic-1020	99	1	or	or	CCONJ
hjic-1020	99	2	near	near	ADP
hjic-1020	99	3	the	the	DET
hjic-1020	99	4	bounds	bound	NOUN
hjic-1020	99	5	.	.	PUNCT
hjic-1020	100	1	the	the	DET
hjic-1020	100	2	best	good	ADJ
hjic-1020	100	3	results	result	NOUN
hjic-1020	100	4	obtained	obtain	VERB
hjic-1020	100	5	by	by	ADP
hjic-1020	100	6	the	the	DET
hjic-1020	100	7	aid	aid	NOUN
hjic-1020	100	8	of	of	ADP
hjic-1020	100	9	the	the	DET
hjic-1020	100	10	genetic	genetic	ADJ
hjic-1020	100	11	algorithm	algorithm	NOUN
hjic-1020	100	12	at	at	ADP
hjic-1020	100	13	the	the	DET
hjic-1020	100	14	end	end	NOUN
hjic-1020	100	15	of	of	ADP
hjic-1020	100	16	500	500	NUM
hjic-1020	100	17	generations	generation	NOUN
hjic-1020	100	18	are	be	AUX
hjic-1020	100	19	exposed	expose	VERB
hjic-1020	100	20	in	in	ADP
hjic-1020	100	21	table	table	NOUN
hjic-1020	100	22	2	2	NUM
hjic-1020	100	23	.	.	PUNCT
hjic-1020	101	1	the	the	DET
hjic-1020	101	2	best	good	ADJ
hjic-1020	101	3	result	result	NOUN
hjic-1020	101	4	obtained	obtain	VERB
hjic-1020	101	5	in	in	ADP
hjic-1020	101	6	[	[	NOUN
hjic-1020	101	7	10j	10j	NUM
hjic-1020	101	8	using	use	VERB
hjic-1020	101	9	the	the	DET
hjic-1020	101	10	classical	classical	ADJ
hjic-1020	101	11	"	"	PUNCT
hjic-1020	101	12	delta	delta	NOUN
hjic-1020	101	13	rule	rule	NOUN
hjic-1020	101	14	"	"	PUNCT
hjic-1020	101	15	as	as	SCONJ
hjic-1020	101	16	learning	learn	VERB
hjic-1020	101	17	procedure	procedure	NOUN
hjic-1020	101	18	corresponds	correspond	VERB
hjic-1020	101	19	to	to	ADP
hjic-1020	101	20	a	a	DET
hjic-1020	101	21	244	244	NUM
hjic-1020	101	22	table	table	NOUN
hjic-1020	101	23	2	2	NUM
hjic-1020	101	24	the	the	DET
hjic-1020	101	25	best	good	ADJ
hjic-1020	101	26	results	result	NOUN
hjic-1020	101	27	obtained	obtain	VERB
hjic-1020	101	28	by	by	ADP
hjic-1020	101	29	the	the	DET
hjic-1020	101	30	aid	aid	NOUN
hjic-1020	101	31	of	of	ADP
hjic-1020	101	32	the	the	DET
hjic-1020	101	33	genetic	genetic	ADJ
hjic-1020	101	34	algorithm	algorithm	NOUN
hjic-1020	101	35	with	with	ADP
hjic-1020	101	36	no	no	ADV
hjic-1020	101	37	-	-	PUNCT
hjic-1020	101	38	seeding	seeding	NOUN
hjic-1020	101	39	and	and	CCONJ
hjic-1020	101	40	with	with	ADP
hjic-1020	101	41	seeding	seed	VERB
hjic-1020	101	42	the	the	DET
hjic-1020	101	43	initial	initial	ADJ
hjic-1020	101	44	population	population	NOUN
hjic-1020	101	45	with	with	ADP
hjic-1020	101	46	a	a	DET
hjic-1020	101	47	chromosome	chromosome	NOUN
hjic-1020	101	48	having	have	VERB
hjic-1020	101	49	rms	rm	NOUN
hjic-1020	101	50	error=	error=	NUM
hjic-1020	101	51	0.1012	0.1012	NUM
hjic-1020	101	52	(	(	PUNCT
hjic-1020	101	53	s;p	s;p	NOUN
hjic-1020	101	54	=	=	SYM
hjic-1020	101	55	500	500	NUM
hjic-1020	101	56	;	;	PUNCT
hjic-1020	101	57	b	b	X
hjic-1020	101	58	=	=	SYM
hjic-1020	102	1	[	[	X
hjic-1020	102	2	-130	-130	X
hjic-1020	102	3	;	;	PUNCT
hjic-1020	102	4	130	130	NUM
hjic-1020	102	5	]	]	PUNCT
hjic-1020	102	6	;	;	PUNCT
hjic-1020	102	7	nh=	nh=	PROPN
hjic-1020	102	8	5	5	NUM
hjic-1020	102	9	;	;	PUNCT
hjic-1020	102	10	no=	no=	ADJ
hjic-1020	102	11	2	2	NUM
hjic-1020	102	12	)	)	PUNCT
hjic-1020	102	13	procedure	procedure	NOUN
hjic-1020	102	14	trial	trial	NOUN
hjic-1020	102	15	number	number	NOUN
hjic-1020	102	16	rms	rm	VERB
hjic-1020	102	17	error	error	NOUN
hjic-1020	102	18	percentage	percentage	NOUN
hjic-1020	102	19	of	of	ADP
hjic-1020	102	20	wrong	wrong	ADJ
hjic-1020	102	21	classifications(%	classifications(%	NOUN
hjic-1020	102	22	)	)	PUNCT
hjic-1020	102	23	no	no	PRON
hjic-1020	102	24	-	-	PUNCT
hjic-1020	102	25	seeding	seed	VERB
hjic-1020	102	26	1	1	NUM
hjic-1020	102	27	0.2816	0.2816	NUM
hjic-1020	102	28	25.00	25.00	NUM
hjic-1020	102	29	no	no	ADV
hjic-1020	102	30	-	-	PUNCT
hjic-1020	102	31	seeding	seed	VERB
hjic-1020	102	32	2	2	NUM
hjic-1020	102	33	0.2506	0.2506	NUM
hjic-1020	102	34	22.58	22.58	NUM
hjic-1020	102	35	no	no	PRON
hjic-1020	102	36	-	-	PUNCT
hjic-1020	102	37	seeding	seed	VERB
hjic-1020	102	38	3	3	NUM
hjic-1020	102	39	0.2466	0.2466	NUM
hjic-1020	102	40	20.77	20.77	NUM
hjic-1020	102	41	o	o	NOUN
hjic-1020	102	42	-	-	ADJ
hjic-1020	102	43	seeding	seed	VERB
hjic-1020	102	44	4	4	NUM
hjic-1020	102	45	0.1445	0.1445	NUM
hjic-1020	102	46	12.10	12.10	NUM
hjic-1020	102	47	no	no	PRON
hjic-1020	102	48	-	-	PUNCT
hjic-1020	102	49	seeding	seed	VERB
hjic-1020	102	50	5	5	NUM
hjic-1020	102	51	0.2446	0.2446	NUM
hjic-1020	102	52	20.97	20.97	NUM
hjic-1020	102	53	o	o	NOUN
hjic-1020	102	54	-	-	ADJ
hjic-1020	102	55	seeding	seed	VERB
hjic-1020	102	56	mean	mean	NOUN
hjic-1020	102	57	0.2336	0.2336	NUM
hjic-1020	102	58	20.32	20.32	NUM
hjic-1020	102	59	seeding	seed	VERB
hjic-1020	102	60	1	1	NUM
hjic-1020	102	61	0.0907	0.0907	NUM
hjic-1020	102	62	3.22	3.22	NUM
hjic-1020	102	63	seeding	seed	VERB
hjic-1020	102	64	2	2	NUM
hjic-1020	102	65	0.0895	0.0895	NUM
hjic-1020	102	66	6.45	6.45	NUM
hjic-1020	102	67	seeding	seed	VERB
hjic-1020	102	68	3	3	NUM
hjic-1020	102	69	0.0902	0.0902	NUM
hjic-1020	102	70	5.65	5.65	NUM
hjic-1020	102	71	seeding	seed	VERB
hjic-1020	102	72	4	4	NUM
hjic-1020	102	73	0.0894	0.0894	NUM
hjic-1020	102	74	6.45	6.45	NUM
hjic-1020	102	75	seeding	seed	VERB
hjic-1020	102	76	·	·	SYM
hjic-1020	102	77	5	5	NUM
hjic-1020	102	78	0.0902	0.0902	NUM
hjic-1020	102	79	4.84	4.84	NUM
hjic-1020	102	80	seeding	seeding	NOUN
hjic-1020	102	81	mean	mean	NOUN
hjic-1020	102	82	0.0900	0.0900	NUM
hjic-1020	102	83	5.32	5.32	NUM
hjic-1020	102	84	fig	fig	NOUN
hjic-1020	102	85	.	.	PUNCT
hjic-1020	103	1	_	_	PUNCT
hjic-1020	104	1	the	the	DET
hjic-1020	104	2	four	four	NUM
hjic-1020	104	3	patterns	pattern	NOUN
hjic-1020	104	4	das	das	PROPN
hjic-1020	104	5	ification	ification	NOUN
hjic-1020	104	6	e	e	NOUN
hjic-1020	104	7	ample	ample	ADJ
hjic-1020	104	8	percentage	percentage	NOUN
hjic-1020	104	9	of	of	ADP
hjic-1020	104	10	wrong	wrong	ADJ
hjic-1020	104	11	cla	cla	PROPN
hjic-1020	104	12	sifi	sifi	NOUN
hjic-1020	104	13	ations	ation	NOUN
hjic-1020	104	14	of	of	ADP
hjic-1020	104	15	9.70	9.70	NUM
hjic-1020	104	16	~	~	PUNCT
hjic-1020	104	17	and	and	CCONJ
hjic-1020	104	18	'	'	PUNCT
hjic-1020	104	19	as	as	SCONJ
hjic-1020	104	20	gi	gi	VERB
hjic-1020	104	21	en	en	INTJ
hjic-1020	104	22	by	by	ADP
hjic-1020	104	23	a	a	DET
hjic-1020	104	24	modified	modify	VERB
hjic-1020	104	25	ba	ba	PROPN
hjic-1020	104	26	k	k	ADJ
hjic-1020	104	27	-	-	PUNCT
hjic-1020	104	28	propagation	propagation	NOUN
hjic-1020	104	29	neural	neural	ADJ
hjic-1020	104	30	netv	netv	ADJ
hjic-1020	104	31	ork	ork	NOUN
hjic-1020	104	32	with	with	ADP
hjic-1020	104	33	neuron	neuron	NOUN
hjic-1020	104	34	in	in	ADP
hjic-1020	104	35	the	the	DET
hjic-1020	104	36	hidden	hide	VERB
hjic-1020	104	37	layer	layer	NOUN
hjic-1020	104	38	neuron	neuron	NOUN
hjic-1020	104	39	in	in	ADP
hjic-1020	104	40	the	the	DET
hjic-1020	104	41	out	out	ADJ
hjic-1020	104	42	ut	ut	PROPN
hjic-1020	104	43	layer	layer	NOUN
hjic-1020	104	44	and	and	CCONJ
hjic-1020	104	45	hyperb	hyperb	NOUN
hjic-1020	104	46	lie	lie	NOUN
hjic-1020	104	47	tangent	tangent	NOUN
hjic-1020	104	48	transfer	transfer	VERB
hjic-1020	104	49	fun	fun	NOUN
hjic-1020	104	50	tion	tion	NOUN
hjic-1020	104	51	.	.	PUNCT
hjic-1020	105	1	it	it	PRON
hjic-1020	105	2	i	i	PRON
hjic-1020	105	3	bvious	bvious	VERB
hjic-1020	105	4	that	that	SCONJ
hjic-1020	105	5	in	in	ADP
hjic-1020	105	6	thi	thi	ADP
hjic-1020	105	7	ca	ca	PROPN
hjic-1020	105	8	e	e	VERB
hjic-1020	105	9	the	the	DET
hjic-1020	105	10	results	result	NOUN
hjic-1020	105	11	gi	gi	VERB
hjic-1020	105	12	en	en	X
hjic-1020	105	13	by	by	ADP
hjic-1020	105	14	genetic	genetic	ADJ
hjic-1020	105	15	algorithm	algorithm	NOUN
hjic-1020	105	16	are	be	AUX
hjic-1020	105	17	wor	wor	PROPN
hjic-1020	105	18	e	e	PROPN
hjic-1020	105	19	than	than	ADP
hjic-1020	105	20	that	that	PRON
hjic-1020	105	21	obtained	obtain	VERB
hjic-1020	105	22	using	use	VERB
hjic-1020	105	23	the	the	DET
hjic-1020	105	24	las	las	PROPN
hjic-1020	105	25	ical	ical	PROPN
hjic-1020	105	26	"	"	PUNCT
hjic-1020	105	27	delta	delta	NOUN
hjic-1020	105	28	rule	rule	NOUN
hjic-1020	105	29	"	"	PUNCT
hjic-1020	105	30	learning	learn	VERB
hjic-1020	105	31	pro	pro	ADJ
hjic-1020	105	32	edure	edure	PROPN
hjic-1020	105	33	.	.	PUNCT
hjic-1020	106	1	due	due	ADP
hjic-1020	106	2	to	to	PART
hjic-1020	106	3	thi	thi	VERB
hjic-1020	106	4	fact	fact	NOUN
hjic-1020	106	5	,	,	PUNCT
hjic-1020	106	6	a	a	DET
hjic-1020	106	7	hybrid	hybrid	ADJ
hjic-1020	106	8	pro	pro	ADJ
hjic-1020	106	9	edure	edure	NOUN
hjic-1020	106	10	was	be	AUX
hjic-1020	106	11	propo	propo	NOUN
hjic-1020	106	12	ed	ed	NOUN
hjic-1020	106	13	:	:	PUNCT
hjic-1020	106	14	in	in	ADP
hjic-1020	106	15	a	a	DET
hjic-1020	106	16	fir	fir	NOUN
hjic-1020	106	17	t	t	PROPN
hjic-1020	106	18	stage	stage	NOUN
hjic-1020	106	19	the	the	DET
hjic-1020	106	20	arch	arch	NOUN
hjic-1020	106	21	is	be	AUX
hjic-1020	106	22	made	make	VERB
hjic-1020	106	23	with	with	ADP
hjic-1020	106	24	the	the	DET
hjic-1020	106	25	aid	aid	NOUN
hjic-1020	106	26	of	of	ADP
hjic-1020	106	27	"	"	PUNCT
hjic-1020	106	28	delta	delta	NOUN
hjic-1020	106	29	rule	rule	NOUN
hjic-1020	106	30	"	"	PUNCT
hjic-1020	106	31	learning	learn	VERB
hjic-1020	106	32	procedure	procedure	NOUN
hjic-1020	106	33	;	;	PUNCT
hjic-1020	106	34	next	next	ADV
hjic-1020	106	35	in	in	ADP
hjic-1020	106	36	the	the	DET
hjic-1020	106	37	second	second	ADJ
hjic-1020	106	38	tage	tage	NOUN
hjic-1020	106	39	the	the	DET
hjic-1020	106	40	initial	initial	ADJ
hjic-1020	106	41	population	population	NOUN
hjic-1020	106	42	of	of	ADP
hjic-1020	106	43	chromo	chromo	ADJ
hjic-1020	106	44	omes	ome	NOUN
hjic-1020	106	45	is	be	AUX
hjic-1020	106	46	eeding	eede	VERB
hjic-1020	106	47	\	\	PROPN
hjic-1020	106	48	!	!	PUNCT
hjic-1020	107	1	rith	rith	PROPN
hjic-1020	107	2	the	the	DET
hjic-1020	107	3	be	be	PROPN
hjic-1020	107	4	t	t	NOUN
hjic-1020	107	5	olution	olution	NOUN
hjic-1020	108	1	fr	fr	INTJ
hjic-1020	108	2	m	m	VERB
hjic-1020	108	3	the	the	DET
hjic-1020	108	4	end	end	NOUN
hjic-1020	108	5	of	of	ADP
hjic-1020	108	6	the	the	DET
hjic-1020	108	7	fir	fir	NOUN
hjic-1020	108	8	t	t	PROPN
hjic-1020	108	9	stage	stage	NOUN
hjic-1020	108	10	.	.	PUNCT
hjic-1020	109	1	th	th	X
hjic-1020	109	2	resul	resul	PROPN
hjic-1020	109	3	obtained	obtain	VERB
hjic-1020	109	4	with	with	ADP
hjic-1020	109	5	thl	thl	PROPN
hjic-1020	109	6	"	"	PUNCT
hjic-1020	109	7	e	e	X
hjic-1020	109	8	ding	ding	NOUN
hjic-1020	109	9	"	"	PUNCT
hjic-1020	109	10	procedure	procedure	NOUN
hjic-1020	109	11	are	be	AUX
hjic-1020	109	12	encouraging	encouraging	ADJ
hjic-1020	109	13	.	.	PUNCT
hjic-1020	110	1	thu	thu	PROPN
hjic-1020	110	2	for	for	ADP
hjic-1020	110	3	th	th	X
hjic-1020	110	4	con	con	NOUN
hjic-1020	110	5	idered	idere	VERB
hjic-1020	110	6	network	network	NOUN
hjic-1020	110	7	.	.	PUNCT
hjic-1020	111	1	"	"	PUNCT
hjic-1020	111	2	delta	delta	NOUN
hjic-1020	111	3	rule	rule	NOUN
hjic-1020	111	4	"	"	PUNCT
hjic-1020	111	5	learning	learn	VERB
hjic-1020	111	6	pr	pr	NOUN
hjic-1020	111	7	edure	edure	NOUN
hjic-1020	111	8	give	give	VERB
hjic-1020	111	9	thew	thew	NOUN
hjic-1020	111	10	ights	ight	NOUN
hjic-1020	111	11	orresponding	orresponde	VERB
hjic-1020	111	12	"	"	PUNCT
hjic-1020	111	13	th	th	X
hjic-1020	111	14	a	a	DET
hjic-1020	111	15	error	error	NOUN
hjic-1020	111	16	of	of	ADP
hjic-1020	111	17	0.1012	0.1012	NUM
hjic-1020	111	18	.	.	PUNCT
hjic-1020	112	1	thi	thi	PART
hjic-1020	112	2	olution	olution	VERB
hjic-1020	112	3	a	a	DET
hjic-1020	112	4	improved	improved	ADJ
hjic-1020	112	5	\i	\i	ADJ
hjic-1020	112	6	ith	ith	PROPN
hjic-1020	112	7	10	10	NUM
hjic-1020	112	8	%	%	NOUN
hjic-1020	112	9	by	by	ADP
hjic-1020	112	10	the	the	DET
hjic-1020	112	11	genetic	genetic	ADJ
hjic-1020	112	12	algorithm	algorithm	NOUN
hjic-1020	112	13	.	.	PUNCT
hjic-1020	113	1	th	th	X
hjic-1020	113	2	re	re	VERB
hjic-1020	113	3	ults	ult	NOUN
hjic-1020	113	4	obtain	obtain	VERB
hjic-1020	113	5	d	d	PROPN
hjic-1020	113	6	ith	ith	NOUN
hjic-1020	113	7	eeding	eede	VERB
hjic-1020	113	8	the	the	DET
hjic-1020	113	9	initial	initial	ADJ
hjic-1020	113	10	population	population	NOUN
hjic-1020	113	11	are	be	AUX
hjic-1020	113	12	gi	gi	INTJ
hjic-1020	113	13	en	en	PROPN
hjic-1020	113	14	al	al	PROPN
hjic-1020	113	15	in	in	ADP
hjic-1020	113	16	table	table	NOUN
hjic-1020	113	17	-1	-1	ADP
hjic-1020	113	18	n	n	CCONJ
hjic-1020	113	19	n	n	CCONJ
hjic-1020	113	20	n	n	PRON
hjic-1020	113	21	"	"	PUNCT
hjic-1020	113	22	'	'	PUNCT
hjic-1020	113	23	"	"	PUNCT
hjic-1020	113	24	'	'	PUNCT
hjic-1020	113	25	"	"	PUNCT
hjic-1020	113	26	'	'	PUNCT
hjic-1020	113	27	"	"	PUNCT
hjic-1020	114	1	=	=	NOUN
hjic-1020	114	2	f	f	X
hjic-1020	115	1	i	i	PRON
hjic-1020	115	2	i	i	PRON
hjic-1020	115	3	~	~	PUNCT
hjic-1020	116	1	i	i	PRON
hjic-1020	116	2	i	i	VERB
hjic-1020	116	3	~	~	PUNCT
hjic-1020	116	4	~	~	PUNCT
hjic-1020	116	5	i	i	PRON
hjic-1020	116	6	~	~	PUNCT
hjic-1020	116	7	~	~	PUNCT
hjic-1020	116	8	rd	rd	X
hjic-1020	116	9	~	~	PUNCT
hjic-1020	116	10	rd	rd	PROPN
hjic-1020	116	11	rd	rd	PROPN
hjic-1020	116	12	connection	connection	PROPN
hjic-1020	116	13	fig.3	fig.3	PROPN
hjic-1020	116	14	changes	change	NOUN
hjic-1020	116	15	of	of	ADP
hjic-1020	116	16	the	the	DET
hjic-1020	116	17	weights	weight	NOUN
hjic-1020	116	18	of	of	ADP
hjic-1020	116	19	the	the	DET
hjic-1020	116	20	inter	inter	ADJ
hjic-1020	116	21	-	-	ADJ
hjic-1020	116	22	neuronal	neuronal	ADJ
hjic-1020	116	23	.	.	PUNCT
hjic-1020	117	1	connections	connection	NOUN
hjic-1020	117	2	between	between	ADP
hjic-1020	117	3	the	the	DET
hjic-1020	117	4	input	input	NOUN
hjic-1020	117	5	and	and	CCONJ
hjic-1020	117	6	hidden	hidden	ADJ
hjic-1020	117	7	layers	layer	NOUN
hjic-1020	117	8	(	(	PUNCT
hjic-1020	117	9	i	i	PRON
hjic-1020	117	10	input	input	VERB
hjic-1020	117	11	;	;	PUNCT
hjic-1020	117	12	h	h	NOUN
hjic-1020	117	13	-hidden	-hidden	PROPN
hjic-1020	117	14	;	;	PUNCT
hjic-1020	117	15	b	b	X
hjic-1020	117	16	-bias	-bia	NOUN
hjic-1020	117	17	)	)	PUNCT
hjic-1020	118	1	9	9	NUM
hjic-1020	118	2	0	0	NUM
hjic-1020	118	3	0	0	NUM
hjic-1020	118	4	0	0	NUM
hjic-1020	118	5	~	~	SYM
hjic-1020	118	6	0	0	NUM
hjic-1020	118	7	c\1	c\1	NOUN
hjic-1020	118	8	c\1	c\1	VERB
hjic-1020	118	9	c\1	c\1	NOUN
hjic-1020	118	10	9	9	NUM
hjic-1020	118	11	0	0	NUM
hjic-1020	118	12	0	0	NUM
hjic-1020	119	1	i	i	PRON
hjic-1020	119	2	c\j	c\j	VERB
hjic-1020	119	3	cj	cj	NOUN
hjic-1020	119	4	,	,	PUNCT
hjic-1020	119	5	.	.	PUNCT
hjic-1020	119	6	;	;	PUNCT
hjic-1020	119	7	.	.	PUNCT
hjic-1020	120	1	rb	rb	NOUN
hjic-1020	121	1	i	i	PRON
hjic-1020	121	2	c\j	c\j	PROPN
hjic-1020	121	3	cj	cj	NOUN
hjic-1020	121	4	,	,	PUNCT
hjic-1020	121	5	i	i	PRON
hjic-1020	121	6	i	i	PRON
hjic-1020	122	1	i	i	PRON
hjic-1020	122	2	i	i	PRON
hjic-1020	123	1	i	i	PRON
hjic-1020	123	2	i	i	PRON
hjic-1020	123	3	connection	connection	VERB
hjic-1020	123	4	fig.4	fig.4	DET
hjic-1020	123	5	changes	change	NOUN
hjic-1020	123	6	of	of	ADP
hjic-1020	123	7	the	the	DET
hjic-1020	123	8	weights	weight	NOUN
hjic-1020	123	9	of	of	ADP
hjic-1020	123	10	the	the	DET
hjic-1020	123	11	inter	inter	ADJ
hjic-1020	123	12	-	-	ADJ
hjic-1020	123	13	neuronal	neuronal	ADJ
hjic-1020	123	14	connections	connection	NOUN
hjic-1020	123	15	between	between	ADP
hjic-1020	123	16	the	the	DET
hjic-1020	123	17	hidden	hide	VERB
hjic-1020	123	18	and	and	CCONJ
hjic-1020	123	19	output	output	NOUN
hjic-1020	123	20	layers	layer	NOUN
hjic-1020	123	21	(	(	PUNCT
hjic-1020	123	22	h­	h­	NOUN
hjic-1020	123	23	hidden	hide	VERB
hjic-1020	123	24	;	;	PUNCT
hjic-1020	123	25	0output	0output	NUM
hjic-1020	123	26	;	;	PUNCT
hjic-1020	123	27	bbias	bbia	NOUN
hjic-1020	123	28	)	)	PUNCT
hjic-1020	123	29	the	the	DET
hjic-1020	123	30	improved	improved	ADJ
hjic-1020	123	31	solution	solution	NOUN
hjic-1020	123	32	was	be	AUX
hjic-1020	123	33	obtained	obtain	VERB
hjic-1020	123	34	mainly	mainly	ADV
hjic-1020	123	35	due	due	ADP
hjic-1020	123	36	to	to	ADP
hjic-1020	123	37	changes	change	NOUN
hjic-1020	123	38	of	of	ADP
hjic-1020	123	39	the	the	DET
hjic-1020	123	40	weights	weight	NOUN
hjic-1020	123	41	of	of	ADP
hjic-1020	123	42	the	the	DET
hjic-1020	123	43	inter	inter	ADJ
hjic-1020	123	44	-	-	ADJ
hjic-1020	123	45	neuronal	neuronal	ADJ
hjic-1020	123	46	connections	connection	NOUN
hjic-1020	123	47	between	between	ADP
hjic-1020	123	48	the	the	DET
hjic-1020	123	49	hidden	hide	VERB
hjic-1020	123	50	and	and	CCONJ
hjic-1020	123	51	output	output	NOUN
hjic-1020	123	52	layers	layer	NOUN
hjic-1020	123	53	(	(	PUNCT
hjic-1020	123	54	figs.3	figs.3	NOUN
hjic-1020	123	55	and	and	CCONJ
hjic-1020	123	56	4	4	NUM
hjic-1020	123	57	)	)	PUNCT
hjic-1020	123	58	.	.	PUNCT
hjic-1020	124	1	for	for	ADP
hjic-1020	124	2	this	this	DET
hjic-1020	124	3	difficult	difficult	ADJ
hjic-1020	124	4	classification	classification	NOUN
hjic-1020	124	5	problem	problem	NOUN
hjic-1020	124	6	,	,	PUNCT
hjic-1020	124	7	the	the	DET
hjic-1020	124	8	mean	mean	ADJ
hjic-1020	124	9	percentage	percentage	NOUN
hjic-1020	124	10	of	of	ADP
hjic-1020	124	11	wrong	wrong	ADJ
hjic-1020	124	12	classifications	classification	NOUN
hjic-1020	124	13	given	give	VERB
hjic-1020	124	14	by	by	ADP
hjic-1020	124	15	this	this	DET
hjic-1020	124	16	hybrid	hybrid	ADJ
hjic-1020	124	17	procedure	procedure	NOUN
hjic-1020	124	18	is	be	AUX
hjic-1020	124	19	with	with	ADP
hjic-1020	124	20	82	82	NUM
hjic-1020	124	21	%	%	NOUN
hjic-1020	124	22	better	well	ADJ
hjic-1020	124	23	than	than	ADP
hjic-1020	124	24	the	the	DET
hjic-1020	124	25	best	good	ADJ
hjic-1020	124	26	solution	solution	NOUN
hjic-1020	124	27	corresponding	correspond	VERB
hjic-1020	124	28	to	to	ADP
hjic-1020	124	29	a	a	DET
hjic-1020	124	30	larger	large	ADJ
hjic-1020	124	31	neural	neural	ADJ
hjic-1020	124	32	network	network	NOUN
hjic-1020	124	33	obtained	obtain	VERB
hjic-1020	124	34	in	in	ADP
hjic-1020	124	35	[	[	X
hjic-1020	124	36	10	10	NUM
hjic-1020	124	37	]	]	PUNCT
hjic-1020	124	38	.	.	PUNCT
hjic-1020	125	1	the	the	DET
hjic-1020	125	2	corresponding	correspond	VERB
hjic-1020	125	3	evolutions	evolution	NOUN
hjic-1020	125	4	of	of	ADP
hjic-1020	125	5	the	the	DET
hjic-1020	125	6	genetic	genetic	ADJ
hjic-1020	125	7	algorithm	algorithm	NOUN
hjic-1020	125	8	for	for	ADP
hjic-1020	125	9	"	"	PUNCT
hjic-1020	125	10	no	no	DET
hjic-1020	125	11	-	-	PUNCT
hjic-1020	125	12	seeding	seeding	NOUN
hjic-1020	125	13	"	"	PUNCT
hjic-1020	125	14	and	and	CCONJ
hjic-1020	125	15	"	"	PUNCT
hjic-1020	125	16	seeding	seed	VERB
hjic-1020	125	17	"	"	PUNCT
hjic-1020	125	18	procedure	procedure	NOUN
hjic-1020	125	19	are	be	AUX
hjic-1020	125	20	repre	repre	VERB
hjic-1020	125	21	ented	ente	VERB
hjic-1020	125	22	in	in	ADP
hjic-1020	125	23	fig.5	fig.5	PROPN
hjic-1020	125	24	,	,	PUNCT
hjic-1020	125	25	and	and	CCONJ
hjic-1020	125	26	respectively	respectively	ADV
hjic-1020	125	27	fig.6	fig.6	PROPN
hjic-1020	125	28	.	.	PUNCT
hjic-1020	126	1	conclusions	conclusion	NOUN
hjic-1020	126	2	the	the	DET
hjic-1020	126	3	genetic	genetic	ADJ
hjic-1020	126	4	algorithms	algorithm	NOUN
hjic-1020	126	5	are	be	AUX
hjic-1020	126	6	an	an	DET
hjic-1020	126	7	exciting	exciting	ADJ
hjic-1020	126	8	way	way	NOUN
hjic-1020	126	9	to	to	PART
hjic-1020	126	10	realize	realize	VERB
hjic-1020	126	11	high	high	ADJ
hjic-1020	126	12	a	a	DET
hjic-1020	126	13	urate	urate	NOUN
hjic-1020	126	14	classification	classification	NOUN
hjic-1020	126	15	multilayer	multilayer	ADJ
hjic-1020	126	16	neural	neural	ADJ
hjic-1020	126	17	networks	network	NOUN
hjic-1020	126	18	.	.	PUNCT
hjic-1020	127	1	different	different	ADJ
hjic-1020	127	2	from	from	ADP
hjic-1020	127	3	other	other	ADJ
hjic-1020	127	4	works	work	NOUN
hjic-1020	127	5	,	,	PUNCT
hjic-1020	127	6	we	we	PRON
hjic-1020	127	7	search	search	VERB
hjic-1020	127	8	for	for	ADP
hjic-1020	127	9	the	the	DET
hjic-1020	127	10	network	network	NOUN
hjic-1020	127	11	tru	tru	PROPN
hjic-1020	127	12	ture	ture	PROPN
hjic-1020	127	13	in	in	ADP
hjic-1020	127	14	an	an	DET
hjic-1020	127	15	external	external	ADJ
hjic-1020	127	16	iterative	iterative	NOUN
hjic-1020	127	17	loop	loop	NOUN
hjic-1020	127	18	.	.	PUNCT
hjic-1020	128	1	we	we	PRON
hjic-1020	128	2	consider	consider	VERB
hjic-1020	128	3	that	that	SCONJ
hjic-1020	128	4	this	this	DET
hjic-1020	128	5	twotage	twotage	NOUN
hjic-1020	128	6	procedure	procedure	NOUN
hjic-1020	128	7	gives	give	VERB
hjic-1020	128	8	a	a	DET
hjic-1020	128	9	better	well	ADJ
hjic-1020	128	10	control	control	NOUN
hjic-1020	128	11	of	of	ADP
hjic-1020	128	12	the	the	DET
hjic-1020	128	13	re	re	NOUN
hjic-1020	128	14	ults	ult	NOUN
hjic-1020	128	15	.	.	PUNCT
hjic-1020	129	1	also	also	ADV
hjic-1020	129	2	,	,	PUNCT
hjic-1020	129	3	this	this	DET
hjic-1020	129	4	procedure	procedure	NOUN
hjic-1020	129	5	allows	allow	VERB
hjic-1020	129	6	the	the	DET
hjic-1020	129	7	selection	selection	NOUN
hjic-1020	129	8	of	of	ADP
hjic-1020	129	9	a	a	DET
hjic-1020	129	10	ne	ne	ADJ
hjic-1020	129	11	tructural	tructural	ADJ
hjic-1020	129	12	variable	variable	NOUN
hjic-1020	129	13	,	,	PUNCT
hjic-1020	129	14	respectively	respectively	ADV
hjic-1020	129	15	the	the	DET
hjic-1020	129	16	number	number	NOUN
hjic-1020	129	17	of	of	ADP
hjic-1020	129	18	neurons	neuron	NOUN
hjic-1020	129	19	in	in	ADP
hjic-1020	129	20	the	the	DET
hjic-1020	129	21	output	output	NOUN
hjic-1020	129	22	layer	layer	NOUN
hjic-1020	129	23	.	.	PUNCT
hjic-1020	130	1	thi	thi	AUX
hjic-1020	130	2	means	mean	VERB
hjic-1020	130	3	different	different	ADJ
hjic-1020	130	4	rules	rule	NOUN
hjic-1020	130	5	of	of	ADP
hjic-1020	130	6	las	las	PROPN
hjic-1020	130	7	es	es	X
hjic-1020	130	8	codification	codification	NOUN
hjic-1020	130	9	,	,	PUNCT
hjic-1020	130	10	and	and	CCONJ
hjic-1020	130	11	the	the	DET
hjic-1020	130	12	equivalent	equivalent	ADJ
hjic-1020	130	13	modifications	modification	NOUN
hjic-1020	130	14	in	in	ADP
hjic-1020	130	15	the	the	DET
hjic-1020	130	16	training	training	NOUN
hjic-1020	130	17	data	data	NOUN
hjic-1020	130	18	sets	set	NOUN
hjic-1020	130	19	.	.	PUNCT
hjic-1020	131	1	to	to	PART
hjic-1020	131	2	realize	realize	VERB
hjic-1020	131	3	these	these	PRON
hjic-1020	131	4	in	in	ADP
hjic-1020	131	5	a	a	DET
hjic-1020	131	6	single	single	ADJ
hjic-1020	131	7	optimization	optimization	NOUN
hjic-1020	131	8	loop	loop	NOUN
hjic-1020	131	9	seems	seem	VERB
hjic-1020	131	10	to	to	PART
hjic-1020	131	11	be	be	AUX
hjic-1020	131	12	a	a	DET
hjic-1020	131	13	little	little	ADJ
hjic-1020	131	14	bit	bit	NOUN
hjic-1020	131	15	complicated	complicated	ADJ
hjic-1020	131	16	.	.	PUNCT
hjic-1020	132	1	aybe	aybe	ADV
hjic-1020	132	2	the	the	DET
hjic-1020	132	3	twotage	twotage	NOUN
hjic-1020	132	4	procedure	procedure	NOUN
hjic-1020	132	5	i	i	PRON
hjic-1020	132	6	many	many	ADJ
hjic-1020	132	7	computer	computer	NOUN
hjic-1020	132	8	time	time	NOUN
hjic-1020	132	9	fig.5	fig.5	VERB
hjic-1020	132	10	the	the	DET
hjic-1020	132	11	evolution	evolution	NOUN
hjic-1020	132	12	of	of	ADP
hjic-1020	132	13	mean	mean	PROPN
hjic-1020	132	14	(	(	PUNCT
hjic-1020	132	15	....	....	PUNCT
hjic-1020	132	16	)	)	PUNCT
hjic-1020	132	17	and	and	CCONJ
hjic-1020	132	18	best	good	ADJ
hjic-1020	132	19	(	(	PUNCT
hjic-1020	132	20	-)fitness	-)fitness	PUNCT
hjic-1020	132	21	for	for	ADP
hjic-1020	132	22	"	"	PUNCT
hjic-1020	132	23	no	no	PRON
hjic-1020	132	24	-	-	PUNCT
hjic-1020	132	25	seeding	seed	VERB
hjic-1020	132	26	"	"	PUNCT
hjic-1020	132	27	procedure	procedure	NOUN
hjic-1020	132	28	consumers	consumer	NOUN
hjic-1020	132	29	,	,	PUNCT
hjic-1020	132	30	but	but	CCONJ
hjic-1020	132	31	our	our	PRON
hjic-1020	132	32	interest	interest	NOUN
hjic-1020	132	33	was	be	AUX
hjic-1020	132	34	focused	focus	VERB
hjic-1020	132	35	on	on	ADP
hjic-1020	132	36	the	the	DET
hjic-1020	132	37	accuracy	accuracy	NOUN
hjic-1020	132	38	of	of	ADP
hjic-1020	132	39	the	the	DET
hjic-1020	132	40	results	result	NOUN
hjic-1020	132	41	.	.	PUNCT
hjic-1020	133	1	it	it	PRON
hjic-1020	133	2	is	be	AUX
hjic-1020	133	3	obvious	obvious	ADJ
hjic-1020	133	4	that	that	SCONJ
hjic-1020	133	5	,	,	PUNCT
hjic-1020	133	6	in	in	ADP
hjic-1020	133	7	order	order	NOUN
hjic-1020	133	8	to	to	PART
hjic-1020	133	9	realize	realize	VERB
hjic-1020	133	10	high	high	ADJ
hjic-1020	133	11	accurate	accurate	ADJ
hjic-1020	133	12	classification	classification	NOUN
hjic-1020	133	13	multilayer	multilayer	ADJ
hjic-1020	133	14	neural	neural	ADJ
hjic-1020	133	15	networks	network	NOUN
hjic-1020	133	16	for	for	ADP
hjic-1020	133	17	practical	practical	ADJ
hjic-1020	133	18	applications	application	NOUN
hjic-1020	133	19	(	(	PUNCT
hjic-1020	133	20	e.g.	e.g.	ADV
hjic-1020	133	21	process	process	NOUN
hjic-1020	133	22	control	control	NOUN
hjic-1020	133	23	or	or	CCONJ
hjic-1020	133	24	expert	expert	NOUN
hjic-1020	133	25	systems	system	NOUN
hjic-1020	133	26	)	)	PUNCT
hjic-1020	133	27	,	,	PUNCT
hjic-1020	133	28	the	the	DET
hjic-1020	133	29	network	network	NOUN
hjic-1020	133	30	performances	performance	NOUN
hjic-1020	133	31	are	be	AUX
hjic-1020	133	32	much	much	ADV
hjic-1020	133	33	more	more	ADV
hjic-1020	133	34	important	important	ADJ
hjic-1020	133	35	than	than	ADP
hjic-1020	133	36	the	the	DET
hjic-1020	133	37	computer	computer	NOUN
hjic-1020	133	38	time	time	NOUN
hjic-1020	133	39	spent	spend	VERB
hjic-1020	133	40	for	for	ADP
hjic-1020	133	41	this	this	PRON
hjic-1020	133	42	.	.	PUNCT
hjic-1020	134	1	another	another	DET
hjic-1020	134	2	direction	direction	NOUN
hjic-1020	134	3	of	of	ADP
hjic-1020	134	4	investigation	investigation	NOUN
hjic-1020	134	5	in	in	ADP
hjic-1020	134	6	this	this	DET
hjic-1020	134	7	work	work	NOUN
hjic-1020	134	8	was	be	AUX
hjic-1020	134	9	the	the	DET
hjic-1020	134	10	performance	performance	NOUN
hjic-1020	134	11	of	of	ADP
hjic-1020	134	12	a	a	DET
hjic-1020	134	13	hybrid	hybrid	ADJ
hjic-1020	134	14	algorithm	algorithm	NOUN
hjic-1020	134	15	composed	compose	VERB
hjic-1020	134	16	from	from	ADP
hjic-1020	134	17	"	"	PUNCT
hjic-1020	134	18	delta	delta	NOUN
hjic-1020	134	19	rule	rule	NOUN
hjic-1020	134	20	"	"	PUNCT
hjic-1020	134	21	and	and	CCONJ
hjic-1020	134	22	genetic	genetic	ADJ
hjic-1020	134	23	algorithms	algorithm	NOUN
hjic-1020	134	24	.	.	PUNCT
hjic-1020	135	1	in	in	ADP
hjic-1020	135	2	fact	fact	NOUN
hjic-1020	135	3	this	this	DET
hjic-1020	135	4	hybridization	hybridization	NOUN
hjic-1020	135	5	was	be	AUX
hjic-1020	135	6	realized	realize	VERB
hjic-1020	135	7	by	by	ADP
hjic-1020	135	8	seeding	seed	VERB
hjic-1020	135	9	the	the	DET
hjic-1020	135	10	initial	initial	ADJ
hjic-1020	135	11	population	population	NOUN
hjic-1020	135	12	from	from	ADP
hjic-1020	135	13	the	the	DET
hjic-1020	135	14	genetic	genetic	ADJ
hjic-1020	135	15	algorithm	algorithm	NOUN
hjic-1020	135	16	with	with	ADP
hjic-1020	135	17	the	the	DET
hjic-1020	135	18	best	good	ADJ
hjic-1020	135	19	solution	solution	NOUN
hjic-1020	135	20	obtained	obtain	VERB
hjic-1020	135	21	with	with	ADP
hjic-1020	135	22	classical	classical	ADJ
hjic-1020	135	23	"	"	PUNCT
hjic-1020	135	24	delta	delta	NOUN
hjic-1020	135	25	rule	rule	NOUN
hjic-1020	135	26	"	"	PUNCT
hjic-1020	135	27	learning	learn	VERB
hjic-1020	135	28	procedure	procedure	NOUN
hjic-1020	135	29	.	.	PUNCT
hjic-1020	136	1	despite	despite	SCONJ
hjic-1020	136	2	the	the	DET
hjic-1020	136	3	warning	warning	NOUN
hjic-1020	136	4	of	of	ADP
hjic-1020	136	5	some	some	DET
hjic-1020	136	6	authors	author	NOUN
hjic-1020	136	7	[	[	X
hjic-1020	136	8	111	111	NUM
hjic-1020	136	9	that	that	PRON
hjic-1020	136	10	by	by	ADP
hjic-1020	136	11	seeding	seed	VERB
hjic-1020	136	12	the	the	DET
hjic-1020	136	13	genetic	genetic	ADJ
hjic-1020	136	14	algorithm	algorithm	NOUN
hjic-1020	136	15	chases	chase	NOUN
hjic-1020	136	16	after	after	ADP
hjic-1020	136	17	a	a	DET
hjic-1020	136	18	local	local	ADJ
hjic-1020	136	19	minimum	minimum	NOUN
hjic-1020	136	20	,	,	PUNCT
hjic-1020	136	21	and	and	CCONJ
hjic-1020	136	22	takes	take	VERB
hjic-1020	136	23	time	time	NOUN
hjic-1020	136	24	to	to	PART
hjic-1020	136	25	find	find	VERB
hjic-1020	136	26	its	its	PRON
hjic-1020	136	27	way	way	NOUN
hjic-1020	136	28	out	out	ADV
hjic-1020	136	29	,	,	PUNCT
hjic-1020	136	30	very	very	ADV
hjic-1020	136	31	good	good	ADJ
hjic-1020	136	32	results	result	NOUN
hjic-1020	136	33	were	be	AUX
hjic-1020	136	34	obtained	obtain	VERB
hjic-1020	136	35	with	with	ADP
hjic-1020	136	36	this	this	DET
hjic-1020	136	37	procedure	procedure	NOUN
hjic-1020	136	38	in	in	ADP
hjic-1020	136	39	a	a	DET
hjic-1020	136	40	difficult	difficult	ADJ
hjic-1020	136	41	classification	classification	NOUN
hjic-1020	136	42	problem	problem	NOUN
hjic-1020	136	43	.	.	PUNCT
hjic-1020	137	1	our	our	PRON
hjic-1020	137	2	research	research	NOUN
hjic-1020	137	3	,	,	PUNCT
hjic-1020	137	4	realized	realize	VERB
hjic-1020	137	5	in	in	ADP
hjic-1020	137	6	the	the	DET
hjic-1020	137	7	frame	frame	NOUN
hjic-1020	137	8	of	of	ADP
hjic-1020	137	9	two	two	NUM
hjic-1020	137	10	applications	application	NOUN
hjic-1020	137	11	,	,	PUNCT
hjic-1020	137	12	finished	finish	VERB
hjic-1020	137	13	with	with	ADP
hjic-1020	137	14	good	good	ADJ
hjic-1020	137	15	results	result	NOUN
hjic-1020	137	16	,	,	PUNCT
hjic-1020	137	17	so	so	SCONJ
hjic-1020	137	18	we	we	PRON
hjic-1020	137	19	can	can	AUX
hjic-1020	137	20	conclude	conclude	VERB
hjic-1020	137	21	that	that	SCONJ
hjic-1020	137	22	the	the	DET
hjic-1020	137	23	use	use	NOUN
hjic-1020	137	24	of	of	ADP
hjic-1020	137	25	the	the	DET
hjic-1020	137	26	genetic	genetic	ADJ
hjic-1020	137	27	algorithms	algorithms	NOUN
hjic-1020	137	28	represents	represent	VERB
hjic-1020	137	29	a	a	DET
hjic-1020	137	30	promising	promising	ADJ
hjic-1020	137	31	way	way	NOUN
hjic-1020	137	32	to	to	PART
hjic-1020	137	33	realize	realize	VERB
hjic-1020	137	34	high	high	ADJ
hjic-1020	137	35	accurate	accurate	ADJ
hjic-1020	137	36	classification	classification	NOUN
hjic-1020	137	37	multilayer	multilayer	ADJ
hjic-1020	137	38	neural	neural	ADJ
hjic-1020	137	39	networks	network	NOUN
hjic-1020	137	40	.	.	PUNCT
hjic-1020	138	1	symbols	symbol	NOUN
hjic-1020	138	2	b	b	PROPN
hjic-1020	138	3	bounds	bound	NOUN
hjic-1020	138	4	imposed	impose	VERB
hjic-1020	138	5	to	to	ADP
hjic-1020	138	6	the	the	DET
hjic-1020	138	7	weights	weight	NOUN
hjic-1020	138	8	of	of	ADP
hjic-1020	138	9	the	the	DET
hjic-1020	138	10	interneuronal	interneuronal	ADJ
hjic-1020	138	11	connections	connection	NOUN
hjic-1020	138	12	nh	nh	PROPN
hjic-1020	138	13	number	number	NOUN
hjic-1020	138	14	of	of	ADP
hjic-1020	138	15	neurons	neuron	NOUN
hjic-1020	138	16	in	in	ADP
hjic-1020	138	17	the	the	DET
hjic-1020	138	18	hidden	hide	VERB
hjic-1020	138	19	layer	layer	NOUN
hjic-1020	138	20	no	no	DET
hjic-1020	138	21	the	the	DET
hjic-1020	138	22	number	number	NOUN
hjic-1020	138	23	of	of	ADP
hjic-1020	138	24	neurons	neuron	NOUN
hjic-1020	138	25	in	in	ADP
hjic-1020	138	26	the	the	DET
hjic-1020	138	27	output	output	NOUN
hjic-1020	138	28	layer	layer	NOUN
hjic-1020	138	29	sip	sip	NOUN
hjic-1020	138	30	size	size	NOUN
hjic-1020	138	31	of	of	ADP
hjic-1020	138	32	initial	initial	ADJ
hjic-1020	138	33	population	population	NOUN
hjic-1020	138	34	xi	xi	ADP
hjic-1020	138	35	variables	variable	NOUN
hjic-1020	138	36	in	in	ADP
hjic-1020	138	37	eqs	eqs	PROPN
hjic-1020	138	38	.	.	PUNCT
hjic-1020	138	39	(	(	PUNCT
hjic-1020	138	40	1)-(3	1)-(3	NUM
hjic-1020	138	41	)	)	PUNCT
hjic-1020	138	42	245	245	NUM
hjic-1020	138	43	fig.6the	fig.6the	DET
hjic-1020	138	44	evolution	evolution	NOUN
hjic-1020	138	45	ofmean	ofmean	NOUN
hjic-1020	138	46	(	(	PUNCT
hjic-1020	138	47	....	....	PUNCT
hjic-1020	138	48	)	)	PUNCT
hjic-1020	138	49	and	and	CCONJ
hjic-1020	138	50	best	good	ADJ
hjic-1020	138	51	(	(	PUNCT
hjic-1020	138	52	-)fitness	-)fitness	PUNCT
hjic-1020	138	53	for	for	ADP
hjic-1020	138	54	"	"	PUNCT
hjic-1020	138	55	seeding	seed	VERB
hjic-1020	138	56	"	"	PUNCT
hjic-1020	138	57	procedure	procedure	NOUN
hjic-1020	138	58	references	reference	NOUN
hjic-1020	138	59	1	1	NUM
hjic-1020	138	60	.	.	PUNCT
hjic-1020	138	61	aldrich	aldrich	PROPN
hjic-1020	138	62	c.	c.	PROPN
hjic-1020	138	63	and	and	CCONJ
hjic-1020	138	64	van	van	PROPN
hjic-1020	138	65	devenier	devenier	PROPN
hjic-1020	138	66	j.	j.	PROPN
hjic-1020	138	67	s.	s.	PROPN
hjic-1020	138	68	j.	j.	PROPN
hjic-1020	138	69	:	:	PUNCT
hjic-1020	138	70	chern	chern	PROPN
hjic-1020	138	71	.	.	PUNCT
hjic-1020	139	1	eng	eng	PROPN
hjic-1020	139	2	.	.	PUNCT
hjic-1020	140	1	sci	sci	PROPN
hjic-1020	140	2	.	.	PROPN
hjic-1020	140	3	,	,	PUNCT
hjic-1020	140	4	1994	1994	NUM
hjic-1020	140	5	,	,	PUNCT
hjic-1020	140	6	49(9	49(9	NUM
hjic-1020	140	7	)	)	PUNCT
hjic-1020	140	8	,	,	PUNCT
hjic-1020	140	9	1357	1357	NUM
hjic-1020	140	10	-	-	SYM
hjic-1020	140	11	1368	1368	NUM
hjic-1020	140	12	2	2	NUM
hjic-1020	140	13	.	.	PUNCT
hjic-1020	140	14	aldrich	aldrich	PROPN
hjic-1020	140	15	c.	c.	PROPN
hjic-1020	140	16	and	and	CCONJ
hjic-1020	140	17	vandeventer	vandeventer	PROPN
hjic-1020	140	18	j.	j.	PROPN
hjic-1020	140	19	s.	s.	PROPN
hjic-1020	140	20	j.	j.	PROPN
hjic-1020	140	21	:	:	PUNCT
hjic-1020	140	22	ind	ind	PROPN
hjic-1020	140	23	.	.	PUNCT
hjic-1020	141	1	eng	eng	PROPN
hjic-1020	141	2	.	.	PROPN
hjic-1020	141	3	chern	chern	PROPN
hjic-1020	141	4	.	.	PUNCT
hjic-1020	142	1	res	re	NOUN
hjic-1020	142	2	.	.	PROPN
hjic-1020	142	3	,	,	PUNCT
hjic-1020	142	4	1995	1995	NUM
hjic-1020	142	5	,	,	PUNCT
hjic-1020	142	6	34(1	34(1	NUM
hjic-1020	142	7	)	)	PUNCT
hjic-1020	142	8	,	,	PUNCT
hjic-1020	142	9	216	216	NUM
hjic-1020	142	10	-	-	SYM
hjic-1020	142	11	224	224	NUM
hjic-1020	142	12	3	3	NUM
hjic-1020	142	13	.	.	PUNCT
hjic-1020	142	14	michalewicz	michalewicz	PROPN
hjic-1020	142	15	z.	z.	PROPN
hjic-1020	142	16	:	:	PUNCT
hjic-1020	142	17	genetic	genetic	ADJ
hjic-1020	142	18	algorithms	algorithm	NOUN
hjic-1020	142	19	+	+	CCONJ
hjic-1020	142	20	data	datum	NOUN
hjic-1020	142	21	structures	structure	NOUN
hjic-1020	142	22	=	=	SYM
hjic-1020	142	23	evolution	evolution	NOUN
hjic-1020	142	24	programs	program	NOUN
hjic-1020	142	25	,	,	PUNCT
hjic-1020	142	26	3rd	3rd	ADJ
hjic-1020	142	27	ed	ed	NOUN
hjic-1020	142	28	.	.	PROPN
hjic-1020	142	29	,	,	PUNCT
hjic-1020	142	30	springer	springer	NOUN
hjic-1020	142	31	verlag	verlag	PROPN
hjic-1020	142	32	,	,	PUNCT
hjic-1020	142	33	berlin	berlin	PROPN
hjic-1020	142	34	,	,	PUNCT
hjic-1020	142	35	1996	1996	NUM
hjic-1020	142	36	4	4	NUM
hjic-1020	142	37	.	.	PUNCT
hjic-1020	142	38	harps	harp	NOUN
hjic-1020	142	39	.	.	PUNCT
hjic-1020	143	1	a.	a.	PROPN
hjic-1020	143	2	,	,	PUNCT
hjic-1020	143	3	samad	samad	PROPN
hjic-1020	143	4	t.	t.	PROPN
hjic-1020	143	5	and	and	CCONJ
hjic-1020	143	6	guha	guha	PROPN
hjic-1020	143	7	a.	a.	PROPN
hjic-1020	143	8	:	:	PUNCT
hjic-1020	143	9	towards	towards	ADP
hjic-1020	143	10	the	the	DET
hjic-1020	143	11	genetic	genetic	ADJ
hjic-1020	143	12	synthesis	synthesis	NOUN
hjic-1020	143	13	of	of	ADP
hjic-1020	143	14	neural	neural	ADJ
hjic-1020	143	15	networks	network	NOUN
hjic-1020	143	16	in	in	ADP
hjic-1020	143	17	:	:	PUNCT
hjic-1020	143	18	davies	davy	NOUN
hjic-1020	143	19	,	,	PUNCT
hjic-1020	143	20	l.	l.	PROPN
hjic-1020	143	21	(	(	PUNCT
hjic-1020	143	22	ed	ed	NOUN
hjic-1020	143	23	.	.	PROPN
hjic-1020	143	24	):	):	PUNCT
hjic-1020	143	25	handbook	handbook	NOUN
hjic-1020	143	26	of	of	ADP
hjic-1020	143	27	genetic	genetic	ADJ
hjic-1020	143	28	algorithms	algorithm	NOUN
hjic-1020	143	29	,	,	PUNCT
hjic-1020	143	30	van	van	PROPN
hjic-1020	143	31	nostrand	nostrand	PROPN
hjic-1020	143	32	reinhold	reinhold	PROPN
hjic-1020	143	33	,	,	PUNCT
hjic-1020	143	34	new	new	PROPN
hjic-1020	143	35	york	york	PROPN
hjic-1020	143	36	,	,	PUNCT
hjic-1020	143	37	1991	1991	NUM
hjic-1020	143	38	5	5	NUM
hjic-1020	143	39	.	.	PUNCT
hjic-1020	144	1	maniezzo	maniezzo	PROPN
hjic-1020	144	2	v.	v.	PROPN
hjic-1020	144	3	:	:	PUNCT
hjic-1020	144	4	lee	lee	PROPN
hjic-1020	144	5	trans	trans	PROPN
hjic-1020	144	6	.	.	PROPN
hjic-1020	144	7	neural	neural	ADJ
hjic-1020	144	8	net	net	PROPN
hjic-1020	144	9	.	.	PROPN
hjic-1020	144	10	,	,	PUNCT
hjic-1020	144	11	1994	1994	NUM
hjic-1020	144	12	,	,	PUNCT
hjic-1020	144	13	5	5	NUM
hjic-1020	144	14	(	(	PUNCT
hjic-1020	144	15	i	i	NOUN
hjic-1020	144	16	)	)	PUNCT
hjic-1020	144	17	,	,	PUNCT
hjic-1020	144	18	e~3	e~3	NOUN
hjic-1020	144	19	.	.	PUNCT
hjic-1020	145	1	6	6	X
hjic-1020	145	2	.	.	X
hjic-1020	145	3	gao	gao	PROPN
hjic-1020	145	4	f.	f.	PROPN
hjic-1020	145	5	,	,	PUNCT
hjic-1020	145	6	lim	lim	PROPN
hjic-1020	145	7	.	.	PROPN
hjic-1020	145	8	,	,	PUNCT
hjic-1020	145	9	wang	wang	PROPN
hjic-1020	145	10	f.	f.	PROPN
hjic-1020	145	11	,	,	PUNCT
hjic-1020	145	12	wang	wang	PROPN
hjic-1020	145	13	b.	b.	PROPN
hjic-1020	145	14	and	and	CCONJ
hjic-1020	145	15	yue	yue	PROPN
hjic-1020	145	16	p.	p.	PROPN
hjic-1020	145	17	l.	l.	PROPN
hjic-1020	145	18	:	:	PUNCT
hjic-1020	145	19	ind	ind	PROPN
hjic-1020	145	20	.	.	PUNCT
hjic-1020	146	1	eng	eng	PROPN
hjic-1020	146	2	.	.	PROPN
hjic-1020	146	3	chern	chern	PROPN
hjic-1020	146	4	.	.	PUNCT
hjic-1020	147	1	res	re	NOUN
hjic-1020	147	2	.	.	PROPN
hjic-1020	147	3	,	,	PUNCT
hjic-1020	147	4	1999	1999	NUM
hjic-1020	147	5	,	,	PUNCT
hjic-1020	147	6	38(11	38(11	NUM
hjic-1020	147	7	)	)	PUNCT
hjic-1020	147	8	,	,	PUNCT
hjic-1020	147	9	4330	4330	NUM
hjic-1020	147	10	-	-	SYM
hjic-1020	147	11	4336	4336	NUM
hjic-1020	147	12	7	7	NUM
hjic-1020	147	13	.	.	PUNCT
hjic-1020	147	14	boozarjombhry	boozarjombhry	PROPN
hjic-1020	147	15	r.	r.	PROPN
hjic-1020	147	16	b.	b.	PROPN
hjic-1020	147	17	and	and	CCONJ
hjic-1020	148	1	svrcek	svrcek	PROPN
hjic-1020	148	2	w.	w.	PROPN
hjic-1020	148	3	y.	y.	PROPN
hjic-1020	148	4	:	:	PUNCT
hjic-1020	148	5	comput	comput	PROPN
hjic-1020	148	6	.	.	PUNCT
hjic-1020	149	1	chern	chern	PROPN
hjic-1020	149	2	.	.	PUNCT
hjic-1020	150	1	engng	engng	PROPN
hjic-1020	150	2	.	.	PUNCT
hjic-1020	150	3	,	,	PUNCT
hjic-1020	150	4	2001	2001	NUM
hjic-1020	150	5	,	,	PUNCT
hjic-1020	150	6	25(10	25(10	NUM
hjic-1020	150	7	)	)	PUNCT
hjic-1020	150	8	,	,	PUNCT
hjic-1020	150	9	1075	1075	NUM
hjic-1020	150	10	-	-	SYM
hjic-1020	150	11	1088	1088	NUM
hjic-1020	150	12	8	8	NUM
hjic-1020	150	13	.	.	PUNCT
hjic-1020	151	1	houck	houck	PROPN
hjic-1020	151	2	c.	c.	PROPN
hjic-1020	151	3	r.	r.	PROPN
hjic-1020	151	4	,	,	PUNCT
hjic-1020	151	5	joines	joines	PROPN
hjic-1020	151	6	j.	j.	PROPN
hjic-1020	151	7	a.	a.	PROPN
hjic-1020	151	8	and	and	CCONJ
hjic-1020	151	9	kay	kay	PROPN
hjic-1020	151	10	m.	m.	PROPN
hjic-1020	151	11	g.	g.	PROPN
hjic-1020	151	12	:	:	PUNCT
hjic-1020	151	13	ncsu­	ncsu­	PROPN
hjic-1020	151	14	ie	ie	X
hjic-1020	151	15	,	,	PUNCT
hjic-1020	151	16	technical	technical	ADJ
hjic-1020	151	17	report	report	NOUN
hjic-1020	151	18	,	,	PUNCT
hjic-1020	151	19	north	north	PROPN
hjic-1020	151	20	carolina	carolina	PROPN
hjic-1020	151	21	state	state	PROPN
hjic-1020	151	22	university	university	PROPN
hjic-1020	151	23	,	,	PUNCT
hjic-1020	151	24	95	95	NUM
hjic-1020	151	25	-	-	SYM
hjic-1020	151	26	09	09	NUM
hjic-1020	151	27	,	,	PUNCT
hjic-1020	151	28	1995	1995	NUM
hjic-1020	151	29	9	9	NUM
hjic-1020	151	30	.	.	PUNCT
hjic-1020	151	31	baughman	baughman	PROPN
hjic-1020	151	32	d.	d.	PROPN
hjic-1020	151	33	r.	r.	PROPN
hjic-1020	151	34	and	and	CCONJ
hjic-1020	151	35	liu	liu	PROPN
hjic-1020	151	36	y.	y.	PROPN
hjic-1020	151	37	a.	a.	PROPN
hjic-1020	151	38	:	:	PUNCT
hjic-1020	151	39	neural	neural	ADJ
hjic-1020	151	40	networks	network	NOUN
hjic-1020	151	41	in	in	ADP
hjic-1020	151	42	bioprocessing	bioprocessing	NOUN
hjic-1020	151	43	and	and	CCONJ
hjic-1020	151	44	chemical	chemical	PROPN
hjic-1020	151	45	engineering	engineering	NOUN
hjic-1020	151	46	,	,	PUNCT
hjic-1020	151	47	academic	academic	ADJ
hjic-1020	151	48	press	press	NOUN
hjic-1020	151	49	,	,	PUNCT
hjic-1020	151	50	san	san	PROPN
hjic-1020	151	51	diego	diego	PROPN
hjic-1020	151	52	,	,	PUNCT
hjic-1020	151	53	1995	1995	NUM
hjic-1020	151	54	10	10	NUM
hjic-1020	151	55	.	.	PUNCT
hjic-1020	152	1	woinaroschy	woinaroschy	PROPN
hjic-1020	152	2	a.	a.	PROPN
hjic-1020	152	3	,	,	PUNCT
hjic-1020	152	4	isopescu	isopescu	PROPN
hjic-1020	152	5	r.	r.	PROPN
hjic-1020	152	6	,	,	PUNCT
hjic-1020	152	7	nita	nita	PROPN
hjic-1020	152	8	i.	i.	PROPN
hjic-1020	152	9	and	and	CCONJ
hjic-1020	152	10	dinu	dinu	PROPN
hjic-1020	152	11	s.	s.	PROPN
hjic-1020	152	12	:	:	PUNCT
hjic-1020	152	13	data	datum	NOUN
hjic-1020	152	14	filtering	filter	VERB
hjic-1020	152	15	via	via	ADP
hjic-1020	152	16	artificial	artificial	ADJ
hjic-1020	152	17	neural	neural	ADJ
hjic-1020	152	18	nets	net	NOUN
hjic-1020	152	19	.	.	PUNCT
hjic-1020	153	1	5lh	5lh	ADJ
hjic-1020	153	2	world	world	PROPN
hjic-1020	153	3	congress	congress	PROPN
hjic-1020	153	4	of	of	ADP
hjic-1020	153	5	chemical	chemical	PROPN
hjic-1020	153	6	engineering	engineering	PROPN
hjic-1020	153	7	,	,	PUNCT
hjic-1020	153	8	san	san	PROPN
hjic-1020	153	9	diego	diego	PROPN
hjic-1020	153	10	,	,	PUNCT
hjic-1020	153	11	vol	vol	NOUN
hjic-1020	153	12	.	.	PUNCT
hjic-1020	154	1	i	i	PRON
hjic-1020	154	2	,	,	PUNCT
hjic-1020	154	3	1011	1011	NUM
hjic-1020	154	4	-	-	SYM
hjic-1020	154	5	1016	1016	NUM
hjic-1020	154	6	,	,	PUNCT
hjic-1020	154	7	1996	1996	NUM
hjic-1020	154	8	11	11	NUM
hjic-1020	154	9	.	.	PUNCT
hjic-1020	155	1	haupt	haupt	PROPN
hjic-1020	155	2	r.	r.	PROPN
hjic-1020	155	3	l.	l.	PROPN
hjic-1020	155	4	and	and	CCONJ
hjic-1020	155	5	haupt	haupt	PROPN
hjic-1020	155	6	s.	s.	PROPN
hjic-1020	155	7	e.	e.	PROPN
hjic-1020	155	8	:	:	PUNCT
hjic-1020	155	9	practical	practical	ADJ
hjic-1020	155	10	genetic	genetic	ADJ
hjic-1020	155	11	algorithms	algorithm	NOUN
hjic-1020	155	12	,	,	PUNCT
hjic-1020	155	13	wiley	wiley	NOUN
hjic-1020	155	14	,	,	PUNCT
hjic-1020	155	15	new	new	ADJ
hjic-1020	155	16	york,1998	york,1998	PROPN
hjic-1020	155	17	page	page	NOUN
hjic-1020	155	18	248	248	NUM
hjic-1020	155	19	page	page	NOUN
hjic-1020	155	20	249	249	NUM
hjic-1020	155	21	page	page	NOUN
hjic-1020	155	22	250	250	NUM
hjic-1020	155	23	page	page	NOUN
hjic-1020	155	24	251	251	NUM
hjic-1020	155	25	page	page	NOUN
hjic-1020	155	26	252	252	NUM