Tran Thi Thu Hoai_ k54c

7/31/2019 Tran Thi Thu Hoai_ k54c

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MC LCI: MNG NRON ..................................................................................................................3

I.1 Gii Thiu Mng Nron ..............................................................................................3I.1.1 Lch s pht trin ..................................................................................................3

I.1.2 Cn nguyn sinh hc ............................................................................................4I.1.3 n v x l ..........................................................................................................6I.1.4 Hm x l ............................................................................................................7I.1.5 ng dng ...........................................................................................................12

I.2 Mng Norn Mt Lp ..............................................................................................12I.3 Mng Noron Nhiu Lp (Multi-layer Neural Network) .........................................13

II: MNG NRON M: ......................................................................................................13III: GII THUT DI TRUYN ............................................................................................16

1: Cc ton t ca gii tht di truyn .................................................................................171.1 Chn lc ....................................................................................................17

1.2 Lai ghp ..................................................................................................................18

1.3 t bin ..........................................................................................................201.4 Hm thch nghi ...............................................................................................212: Xt trong mi quan h gia mng nron v gii thut di truyn .................................22

2.1 Cross-over (Lai ghp) ..............................................................................................232.2 Mutation (t bin) ................................................................................................242.3 Fitness function (Hm thch nghi) ...........................................................................242.4 Selection (chn lc) ................................................................................................26

3: Chin lc iu chnh m t ng ................................................................................26IV: KT LUN ......................................................................................................................27


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ti: Ti u ho cu trc ca mng nron m bng gii thut di truyn

BO CO NGHIN CU KHOA HC

Tn ti: TI U HO CU TRC CA MNG NRON M BNGGII THUT DI TRUYN

M U

L do chn ti: Gn y suy din m c ng dng trong rt nhiu cc

vn khc nhau nh: iu khin my mc hay trong cc h thng sn xut.

Mt trong nhng suy din m l mng nron m. C l mng noron

khng ch hp dn i vi nhng ngi yu thch cng ngh thng tin bi

kh nng do con ngi hun luyn, m cn bi nhng ng dng thc tintrong cuc sng ca n. Chng ta hon ton c th nhn dng du vt vn

tay ca ti phm trong hnh s, c th d on th trng chng khon, d

on thi tit, d ton chi ph cho mt d n ng cao tc, khi phc

nhng tm nh, hay mt chic xe ln dnh cho ngi khuyt tt c th nhn

c mnh lnh iu khin bng c ch, hnh ng, thm ch l suy ngh ca

ngi ngi trn xe v.v nh c mng noron nhn to. Mng nron ban u

c cu trc th, vn quan trng l chng ta phi lm sao cho cu trc th

tr thnh cu trc tng i thch hp. Do vn ti u ho cu trc

ca mng nron l rt cn thit. Mt trong nhng gii thut dng ti u

ho cu trc ca mng nron l gii thut di truyn v gii thut di truyn

c xem l thch hp nht.

Sinh vin: Trn Th Thu Hoi_K54C_CNTT 2


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I: MNG NRON

I.1 Gii Thiu Mng Nron

I.1.1 Lch s pht trin

S pht trin ca mng nron tri qua c qu trnh a ra cc khi nimmi ln thc thi cc khi nim ny. Di y l cc mc ng ch trong

lch s pht trin ca mng nron.

* Cui th k 19, u th k 20, s pht trin ch yu ch l cc cng

vic c s tham gia ca c ba ngnh Vt l hc, Tm l hc v Thn kinh

hc, bi cc nh khoa hc nh Hermann von Hemholtz, Ernst Mach, Ivan

Pavlov. Cc cng trnh nghin cu ca h ch yu i su vo cc l thuyt

tng qut v HC (learning), NHN (vision) v LP LUN (conditioning),v khng h a ra nhng m hnh ton hc c th m t hot ng ca

cc nron.

* Mi chuyn thc s bt u vo nhng nm 1940 vi cng trnh ca

Warren McCulloch v Walter Pitts. H ch ra rng v nguyn tc, mng ca

cc nron nhn to c th tnh ton bt k mt hm s hc hay logic no.

* Tip theo l Donald Hebb, ng pht biu rng vic thuyt lp

lun c in (classical conditioning) (nh Pavlov a ra) l hin thc bi do

cc thuc tnh ca tng nron ring bit. ng cng nu ra mt phng php

hc ca cc nron nhn to.

* ng dng thc nghim u tin ca cc nron nhn to c c vo

cui nhng nm 50 cng vi pht minh ca mng nhn thc (perceptron

network) v lut hc tng ng bi Frank Rosenblatt. Mng ny c kh

nng nhn dng cc mu. iu ny m ra rt nhiu hy vng cho vic nghin

cu mng nron. Tuy nhin n c hn ch l ch c th gii quyt mt s lp

hu hn cc bi ton.* Cng thi gian , Bernard Widrow v Ted Hoff a ra mt thut

ton hc mi v s dng n hun luyn cho cc mng nron tuyn tnh

thch nghi, mng c cu trc v chc nng tng t nh mng ca

Rosenblatt. Lut hc Widrow-Hoff vn cn c s dng cho n nay.



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* Tuy nhin c Rosenblatt v Widrow-Hoff u cng vp phi mt vn

do Marvin Minsky v Seymour Papert pht hin ra, l cc mng nhn

thc ch c kh nng gii quyt cc bi ton kh phn tuyn tnh. H c gng

ci tin lut hc v mng c th vt qua c hn ch ny nhng h

khng thnh cng trong vic ci tin lut hc c th hun luyn c cc

mng c cu trc phc tp hn.

* Do nhng kt qu ca Minsky-Papert nn vic nghin cu v mng

nron gn nh b nh li trong sut mt thp k do nguyn nhn l khng

c c cc my tnh mnh c th thc nghim.

* Mc d vy, cng c vi pht kin quan trng vo nhng nm 70. Nm

1972 Teuvo Kohonen v James Anderson c lp nhau pht trin mt loi

mng mi c th hot ng nh mt b nh. Stephen Grossberg cng rttch cc trong vic kho st cc mng t t chc (Self organizing network).

* Vo nhng nm 80, vic nghin cu mng nron pht trin rt mnh

m cng vi s ra i ca PC. C hai khi nim mi lin quan ti s hi

sinh ny, l:

+ Vic s dng cc phng php thng k gii thch hot

ng ca mt lp cc mng hi quy (recurrent network) c th

c dng nh b nh lin hp (associative memory) trongcng trnh ca nh vt l hc Johh Hopfield.

+ S ra i ca thut ton lan truyn ngc (back-

propagation) luyn cc mng nhiu lp c mt vi nh

nghin cu c lp tm ra nh: David Rumelhart, James

McCelland, cng l cu tr li cho Minsky-Papert.

I.1.2 Cn nguyn sinh hcB no con ngi cha khong 1011 cc phn t lin kt cht ch vi

nhau (khong 104 lin kt i vi mi phn t) gi l cc nron. Di con

mt ca nhng ngi lm tin hc, mt nron c cu to bi cc thnh

phn: t bo hnh cy (dendrite), t bo thn (cell body) v si trc thn kinh



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(axon). T bo hnh cy c nhim v mang cc tn hiu in ti t bo thn,

t bo thn s thc hin gp (sum) v phn ngng (threshold) cc tn hiu

n. Si trc thn kinh lm nhim v a tn hiu t t bo thn ra ngoi.

im tip xc gia mt si trc thn kinh ca nron ny v t bo hnh cy

ca mt nron khc c gi l khp thn kinh (synapse). s sp xp ca

cc nron v mc mnh yu ca cc khp thn kinh c quyt nh bi

cc qu trnh ho hc phc tp, s thit lp chc nng ca mng nron.

Mt vi nron c sn t khi sinh ra, cc phn khc c pht trin thng qua

vic hc, c s thit lp cc lin kt mi v loi b cc lin kt c.

Cu trc ca mng nron lun lun pht trin v thay i. Cc thay i sau

ny c khuynh hng bao gm ch yu l vic lm tng hay gim mnh

ca cc mi lin kt thng qua cc khp thn kinh.

Mng nron nhn to khng tip cn n s phc tp ca b no. Mc d

vy c hai s tng quan c bn gia mng nron nhn to v sinh hc. Th

nht, cu trc khi to thnh chng u l cc thit b tnh ton n gin(mng nron nhn to n gin hn nhiu) c lin kt cht ch vi nhau.

Th hai, cc lin kt gia cc nron quyt nh chc nng ca mng.

Cn ch rng mc d mng nron sinh hc hot ng rt chm so vi cc

linh kin in t (10-3 so vi 10-9 giy) nhng b no c kh nng thc hin

nhiu cng vic nhanh hn nhiu so vi cc my tnh thng thng. mt

phn l do cu trc song song ca mng nron sinh hc: ton b cc nron

hot ng mt cch ng thi ti mt thi im. Mng nron nhn to cng

chia s c im ny. Mc d hin nay, cc mng nron ch yu c thc

nghim trn cc my tnh s, nhng cu trc song song ca chng khin

chng ta c th thy cu trc ph hp nht l thc nghim chng trn cc vi



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mch tch hp ln (VLSI: very large scale integrated circuit), cc thit b

quang v cc b x l song song.

Mng nron i khi c xem nh l cc m hnh lin kt (connectionist

models), l cc m hnh phn b song song (parallel-distributed models) c

cc c trng phn bit sau:

* Tp cc n v x l;

* Trng thi kch hot hay l u ra ca n v x l;

* Lin kt gia cc n v. Xt tng qut, mi lin kt c

nh ngha bi mt trng s wjk cho ta bit hiu ng m tn

hiu ca n v j c trn n v k;* Mt lut lan truyn quyt nh cch tnh tn hiu ra ca

tng n v u vo ca n;

* Mt hm kch hot, hay hm chuyn (activation function,

transfer function), xc nh mc kch hot khc da trn

mc kch hot hin ti;

* Mt n v iu chnh ( lch) (bias, offset) ca mi n

v;* Phng php thu thp thng tin (lut hc- learning rule);

* Mi trng h thng c th hot ng

I.1.3 n v x lMt n v x l (Hnh 1) cng c gi l mt nron hay mt nt

(node), thc hin mt cng vic rt n gin: n nhn tn hiu vo t cc

n v pha trc hay mt ngun bn ngoi v s dng chng tnh tnhiu ra s c lan truyn sang cc n v khc



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Trong :

xi : cc u vo

wji : cc trng s tng ng vi cc u vo

j : lch (bias)aj : u vo mng (net-input)

zj : u ra ca nron

g(x) : hm chuyn (hm kch hot).

Trong mt mng nron c ba kiu n v:

* Cc n v u vo (input units), nhn tn hiu t bn ngoi;

* C n v u ra (output units), gi d liu ra bn ngoi;

* Cc n v n (hidden units), tn hiu vo (input) v ra(output) ca n nm trong mng.

Mi n v j c th c mt hoc nhiu u vo: x 0, x1, x2,xn, nhng ch c

mt u ra zj. Mt u vo ti mt n v c th l d liu t bn ngoi

mng, hoc u ra ca mt n v khc, hoc l u ra ca chnh n.

I.1.4 Hm x l

Hm kt hp

Mi mt n v trong mt mng kt hp cc gi tr a vo n thng

qua cc lin kt vi cc n v khc, sinh ra mt gi tr gi l net-input.

Hm thc hin nhim v ny gi l hm kt hp (combination



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function), c nh ngha bi mt lut lan truyn c th. Trong phn

ln cc mng nron, chng ta gi s rng mi mt n v cung cp mt

b cng nh l u vo cho n v m n c lin kt. Tng u vo n

v j n gin ch l tng trng s ca cc u ra ring l t cc n v

kt ni cng thm ngng hay lch j

aj = ji1

wn

i j

i

x =

+

Trng hp wji > 0, nron c coi l ang trong trng thi kch

thch. Tng t, nu nh wji < 0, nron trng thi kim ch. Chng ta

gi cc n v vi lut lan truyn nh trn l cc sigma units. Trong

mt vi trng hp ngi ta cng c th s dng cc lut lan truyn

phc tp hn. Mt trong s l lut sigma-pi, c dng nh sau:

aj = ji1

wn

i

i

x=

1

m

ik j

k

x =

+

Rt nhiu hm kt hp s dng mt lch hay ngng

tnh net-input ti n v. i vi mt n v u ra tuyn tnh, thng

thng j c chn l hng s v trong bi ton xp x a thc j =1

Hm kch hot (hm chuyn)

Phn ln cc n v trong mng nron chuyn net-input bng cch s

dng mt hm v hng (scalar-to-scalar function) gi l hm kch

hot, kt qu ca hm ny l mt gi tr gi l mc kch hot ca

n v (units activation). Loi tr kh nng n v thuc lp ra, gi

tr kch hot c a vo mt hay nhiu n v khc. Cc hm kch

hot thng b p vo mt khong gi tr xc nh, do thng c

gi l cc hm bp (squashing). Cc hm kch hot hay c s dng

l:

+ Hm ng nht (Linear function, Identity function)

g(x) = x



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Nu coi cc u vo l mt n v th chng s s dng hm ny. i

khi mt hng s c nhn vi net-input to ra mt hm ng nht

+ Hm bc nh phn (binary step function, hard limit function)

Hm ny cng c bit n vi tn hm ngng. u ra ca hm

ny c gii hn vo mt trong hai gi tr:

1 nu (x 0)

g(x) =

0 nu (x 0)

Dng hm ny c s dng trong cc mng ch c mt lp. Trong

hnh v sau, c chn bng 1.



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+ Hm sigmoid (Sigmoid function (logsig))

1

( )1 x

g xe

=+

Hm ny c bit thun li khi s dng cho cc mng c hun luyn

(trained) bi thut ton lan truyn ngc (back-propagation) bi v n

d ly o hm, do c th gim ng k tnh ton trong qu trnh

hun luyn. Hm ny c ng dng trong cc chng trnh ng dng

m cc u ra mong mun ri vo khong [0,1].

+ Hm sigmoid lng cc (Bipolar sigmoid function (tansig))

1

( )1

x

x

eg x

e

=

+

Hm ny c cc thuc tnh tng t hm sigmoid. N lm vic tt i

vi cc ng dng c u ra yu cu trong khong [-1,1].



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Cc hm chuyn ca cc n v n (hidden units) l cn thit biu

din s phi tuyn vo trong mng. L do l hp thnh ca cc hm

ng nht l mt hm ng nht. Mc d vy nhng n mang tnh cht

phi tuyn (ngha l kh nng biu din cc hm phi tuyn) lm cho cc

mng nhiu tng c kh nng rt tt trong biu din cc nh x phi

tuyn. Tuy nhin i vi lut hc lan truyn ngc, hm phi kh vi v

s c ch nu nh hm c gn trong mt khong no . Do vy hm

sigmoid l la chn thng dng nht.

i vi cc n v u ra (output units), cc hm chuyn cn c chnsao cho ph hp vi s phn phi ca cc gi tr ch mong mun.

Chng ta thy rng i vi cc gi tr ra trong khong [0,1], hm

sigmoid l c ch; i vi cc gi tr ch mong mun l lin tc trong

khong th hm ny cng vn c ch, n c th cho ta cc gi tr ra

hay gi tr ch c cn trong mt khong ca hm kch hot u ra.

Nhng nu cc gi tr ch khng c bit trc khong xc nh th

hm hay c s dng nht l hm ng nht. Nu gi tr mong munl dng nhng khng bit cn trn th nn s dng mt hm kch hot

dng m (exponential output activation function).



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I.1.5 ng dngTrong qu trnh pht trin, mng nron c ng dng

thnh cng trong rt nhiu lnh vc. Di y l mt s lnh vc ng

dng chnh ca mng nron:

Aerospace: Phi cng t ng, gi lp ng bay, cc h thng

iu khin li my bay, b pht hin li.

Automotive: Cc h thng dn ng t ng cho t, cc b

phn tch hot ng ca xe.

Banking: B c sc v cc ti liu, tnh tin ca th tn dng.

Defense: nh v- pht hin v kh, d mc tiu, pht hin i

tng, nhn dng nt mt, cc b cm bin th h mi, x l

nh radar,

Electronics: D on m tun t, s chip IC, iu khin tin

trnh, phn tch nguyn nhn hng chip, nhn dng ting ni,

m hnh phi tuyn.

Entertaiment: Hot hnh, cc hiu ng c bit, d bo th

trng.

Financial: nh gi bt ng sn, cho vay, kim tra ti sn cm

c, nh gi mc hp tc, phn tch ng tn dng, chngtrnh thng mi qua giy t, phn tch ti chnh lin doanh, d

bo t l tin t.

Insurance: nh gi vic p dng chnh sch, ti u ho sn

phm.

I.2 Mng Norn Mt Lp

Mng Perceptron

Mng Hopfield



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Mng Kiu B Nh Hai Chiu Kt Hp Thch Nghi (Adaptive

Bidirectional Associative)

Mng Kohonen

I.3 Mng Noron Nhiu Lp (Multi-layer Neural Network)Mng nron c t 2 lp tr ln c gi l mng nron nhiu lp. Mng

nron nhiu lp bao gm mt lp vo, mt lp ra, mt hoc nhiu lp n

Mng noron nhiu lp lan truyn ngc sai s(Back- propagation

Neural Network)

Mng noron nhiu lp ngc hng (Counter propagation Neural

Network)

II: MNG NRON M:Vic tch hp k thut mng nron v logic m cho php kt hp u

im ca c hai. Mt mt, mng nron cung cp cu trc tnh ton da trnlin kt (dung th li v cc tnh cht biu din phn tn) v kh nng hc

cho cc h logic m. Mt khc, cc h logic m s a vo mng nron c

ch suy din da trn cc lut ifthen, chnh s kt hp phong ph ny

cho php xy dng cc h thng tch hp: H m nron, mng nron m v

cc h lai.

Trong mng nron m c th l tn hiu vo, tn hiu ra hay trng s l

nhng s m... Cng c trng hp mng nron m vi tt c cc yu t.

*Mng nron nh mt cng c suy din

Ni mng nron nh mt cng c suy din v: mng nron c kh nng

suy din. Vi mi tn hiu vo th mng nron s cho mt u ra tng ng

* Suy din m da trn mng nron:

Biu din lut m:



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Keller (1992) xut m hnh mng nron truyn thng nhiu lp

biu din cc lut suy din c s:

If X1 = A1 then Y = B

Gi s vc t thuc ca Ai l {ai1,, aimi}, I = 1 n. C hai cch

xc nh trng s:


a11

' a1m1

' an1

' anmn

'

w11

w1m1

wn1

wnm

d1

dn

1-t

u1

uk

b1

' bk

'

Lp vo

Lp kimtra tngmnh

Kt hp ccmnh

Lp ra

Mng noron biu din mt lut m


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Cch 1: ijw = 1 - ija . Khi lp n u tin o s bt cp gia

thng tin vo vi Ai.

Ta xc nh di nh l di = max{(1- ija

) ij'a}

Hoc di = max{min(1- ija ), ij'a }

y Ai = {ai1,,aimi}l vc t vo tng ng vi mnh

X= Ai

Cch 2 : ij ijw a= . Khi , dj = max{| ija = ij 'a |}

Cc h s ai xc nh trng s ca mnh X = Ai trong lut (ai

c th c ngi thit k cung cp hoc c t d liu)Ta thy t = max {aidi}.

Gi s B tng ng vi tp m c thuc {b1,, bk}. Khi

trng s ui c xc nh bi ui = 1- bi.

Cui cng vi b u vo (A1,, An) ta c kt qu:

( ), 1,B jy j p = (*)

T (*) d dng thy t=0 (tc l b u vo (A1,, An) trng

vi (A1, , An) th bi= bi) vi mi i ngha l Y=B.

Ngc li, khi tng bt cp gia thng tin vo vi cc mnh

bng 1 th bi = 1 i vi mi i, ngha l h a ra kt lun:

Y = khng bit.

Suy din m:

M hnh suy din m da trn mng nron do Takagi v

Hayaki ngh l mt bin th ca c ch suy din m ca

Takagi-Sugeno-Kang bao gm cc bc nh sau:

+ La chn bin vo ra trong tp mu hc.

+ Chia tp mu hc thnh hai phn: phn hun luyn v

phn kim tra.



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+ Xy dng mng NNmemb biu th hm ph thuc

cho phn IF ca cc lut. Hun luyn mng NNmemb tng ng

vi phn IF ca lut m.

+ n gin phn THEN ca cc lut theo phng php

loi b ngc.

+ Xc nh kt qu ra v din gii m.

III: GII THUT DI TRUYNGii thut di truyn (Genetic Algorithsm- GA) l k thut gip gii quyt

bi ton bng cch m phng theo s tin ho ca con ngi hay ca sinh

vt ni chung (Da trn thuyt tin ho con ngi ca Darwin) trong iukin mi trng sng lun thay i. Thut gii di truyn l mt hng tip

cn tnh ton gn ng, ngha l mc tiu ca thut gii di truyn khng

nhm a ra li gii chnh xc ti u m l a ra li gii tng i ti u.

L thuyt ny do Johm Henry Holland (Gio s ca trng i hc

Michigan- M) xng vo gia thp nin 70, th k XX.

Thut gii di truyn v bn cht l thut ton tm kim da theo quy lut

ca qu trnh tin ho t nhin. Gii thut kt hp s sng st ca cu trckho nht trong s cc cu trc biu din cc nhim sc th vi mt s trao

i thng tin c la chn ngu nhin to thnh mt thut ton tm

kim. Gii thut di truyn nm trong lnh vc tnh ton tin ho, s dng cc

biu din nh phn v cc s m hnh ho s chn lc, lai ghp v t

bin.

Gii thut di truyn gii quyt c vn trn my tnh nh vo

chng trnh tin hc thc hin nhng tng nu trn. Khng ging nh

phng php gii tch da trn cc cng thc ton hc hay phng php suy

lun da trn kinh nghim ca cc chuyn gia ch ti mt s c gii hn

cc li gii. Gii thut di truyn xt n ton b cc li gii, bng cch xt

trc nht mt s li gii, sau loi b mt s thnh phn khng thch hp



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v chn nhng thnh phn thch nghi hn chn lc v bin ho nhm mc

ch to ra nhiu li gii mi c h s thch nghi ngy cng cao

1: Cc ton t ca gii tht di truyn Giithut di truyn s dng ba ton t sau y:

Chn lc

Lai ghp

t bin

1.1 Chn lcChn lc l qu trnh trong cc c th c sao chp trn c s thch nghi ca n. thch nghi l mt hm gn mt gi tr thc cho

cc c th trong qun th. Qu trnh ny c m t nh sau:

* Tnh thch nghi ca tng c th trong qun th hin hnh, lp

bng cng dn cc gi tr thch nghi (theo s th t gn cho tng c

th). Gi s qun th c n c th, gi thch nghi ca c th th I l Fi,

tng dn th i l Fti, tng thch nghi ca ton qun th l Fm.

* To mt s ngu nhin F trong on t 0 n Fm

* Chn c th k u tin tho mn Ftk-1 F Ftk a vo qun th ca

th h mi.

V d: Gi s qun th ban u l 6 chui nhim sc th. Tng gi tr

ca hm mc tiu l 50 nh bng sau:

STT Chui nhim scth

Hm mctiu

% ca total total

1 01110 8 16 82 11000 15 30 233 00100 2 4 254 10010 5 10 30



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5 01100 12 24 426 00010 8 16 50

Sau ta s to cc s ngu nhin trong khong (0, 50), vi mi s,

vic chn lc mt nhim sc th u tin vi gi tr hm mc tiu lnhn hay bng s ngu nhin. By s ngu nhin c to cng cc

chui c chn th hin nh bng sau:

S ngu nhin 26 2 49 15 40 36 9Chui nhim sc

th

4 1 6 2 5 5 2

V d trn chng t rng chui no c gi tr hm mc tiu cao s

c nhiu con chu hn trong th h sau. Khi mt chui c chn

cho qu trnh ti to th s c a vo lai ghp nhm to ra nhng

chui mi.

1.2 Lai ghpTon t chn lc nhm tm ra nhng c th tn ti tt nht nhng n

khng to ra nhng c th mi. Tuy vy, trong t nhin, cc con s tha

hng nhng c tnh tt t c cha ln m chng. Ton t tc ng trn

cc c th cha m to ra nhng con lai tt c gi l lai ghp.

Chng c p dng ln cp cha m c chn la vi xc sut lai

ghp pcross. Xc sut ny cho ta s lng pcross* popsize (popsize kch

thc ca qun th c lai to) nhim sc th c lai ghp.

Vi mi nhim sc th trong qun th:

Pht sinh mt s ngu nhin r trong min [0;1]

Nu r < pcross, chn nhim sc th lai ghp

Sau , ta kt hp cc nhim sc th c chn mt cch ngu nhin

li. Mi cp nhim sc th, chng ta c th pht sinh mt s ngu



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nhin pos t min [1;L] (L l tng s bt trong nhim sc th). S

pos ny s cho ta v tr ca im lai ghp.

V d ta c 2 nhim sc th:

(a1 a2 aposapos+1aL) v

(b1 b2 bposbpos+1bL)

Sau khi c lai ghp, n s c thay th bi cp con chu

(a1 a2 apos bpos+1bL) v

(b1 b2 bpos apos+1aL)

Nh vy ton t ny sau khi c thc hin s cho ra hai chui

mi, mi chui u c tha hng nhng c tnh ly ra t cha vm ca chng. Chn lc c th v lai ghp cho php gii thut di truyn

s dng nhng thng tin c tm kim trc tip trn nhng vng

tt hn.

Cc v d di y th hin cc hnh thc ca lai ghp:

V d 1

Trc khi lai ghp

1001101

0000110

Sau khi lai ghp ti v tr gia s th t 4 v 5, chng ta s c:

(A) 100| 1101 000| 1101 (B)

(B) 000| 0110 100| 0110 (A)

V d 2

Trc khi lai ghp

1001101



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0000110

Sau khi lai ghp ti v tr gia s th t 3 v 4, chng ta s c:

(A) 1001| 101 0000| 101 (B)

(B) 0000| 110 1001| 110 (A)

1.3 t binL vic thay i tr s ca mt s trong dy s, th d 0 thnh 1

hoc 1 thnh 0, cho trng hp dng dy s theo h nh phn. So vi lai

ghp, ton t ny rt t xy ra. Theo kt qu nghin cu ca Kenneth De

Jong th t l lai ghp trung bnh l 0.6 trong khi t l t bin l 0.001,phn cn li 0.399 l chn lc

Lai ghp dng li nhng tin tc c sn trong cc thnh phn ca

th h trc v truyn li cho th h sau, trong khi t bin to ra

nhng tin tc hon ton mi.

Cc ton t t bin nhm to ra nhng thng tin mi trong qun

th em lai to ti cc v tr bt (gen) no (qun th m ta xem xt

y c popsize c th, mi c th c biu th qua L bt/gen). t bin

c p dng vi xc sut pmu. S lng bt t bin l pmu*L*popsize

bt. Mi bt c c hi t bin l nh nhau. Ton t ny c th c x

l nh sau:

Vi mi nhim sc th trong qun th v mi bt trong nhim sc

th :

Pht sinh mt s ngu nhin r trong min [0;1].

Nu r < pmu, tin hnh t bin ti bt .

Cc thao tc x l ny c p dng lp li cho ti khi cc c thcon, chu ca chng tng trng ti kch c mong mun ca qun th.

V d 1:

100111 s c t bin thnh 100110, trong s 1 hng

cui (tnh t tri) c i thnh 0.



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V d 2:

110110 s c bin i thnh 111110, trong s 0 v

tr th 3 (tnh t tri) c i thnh 1.

1.4 Hm thch nghiI.4.1 nh x gi tr hm mc tiu sang gi tr thch nghi

V hm thch nghi phi nhn gi tr khng m, do cn

phi xy dng nh x hm mc tiu ang xt trong bi ton sang

hm thch nghi thng qua mt hoc nhiu ln nh x.

Nu bi ton ti u l cc tiu mt hm nh gi g(x), vicchuyn t hm nh gi sang hm thch nghi s dng vi gii

thut di truyn nh sau:

Cmax g(x) khi g(x) < Cmax

f(x) =

0 trong cc trng hp khc

y, Cmax l mt tham s u vo. V d, c th ly Cmax l

gi tr ln nht ca g(x) trong qun th hin ti hoc c th l gi

tr ln nht sau k vng lp. Ni chung Cmax khc nhau tu thuc

vo gi tr cc bin ca qun th

Nu hm mc tiu gc tng hoc ang xt bi ton cc i

ho mt hm u(x) no , ta c th chuyn sang hm thch nghi

nh sau:

Cmin + u(x) khi u(x) < Cmin

f(x) =

0 trong cc trng hp khc



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y, Cmin l tham s u vo, c th l tr tuyt i ca

u(x) b nht trong qun th hin ti hoc trong k vng lp cui

cng hoc l mt hm ca bin qun th.

I.4.2 iu chnh thch nghi

Mt vn quan trng l iu chnh s con chu. iu ny

c bit quan trng cho mt vi vng lp u tin, khi mt vi c

th siu c tim nng chim lnh phn ln qun th v lm cho

hi t sm. iu chnh thch nghi c th gip gii quyt vn

ny.

C nhiu kiu iu chnh khc nhau, tuy nhin iu chnh

tuyn tnh l hay gp nht. Gi f l thch nghi gc, f l thchnghi c bin i. thch nghi theo iu chnh tuyn tnh

c xc nh theo quy tc:

f = a*f + b

Trong , h s a, b c xc nh sao cho:

favg = favg

V fmax = Cmult*favg

y, Cmult l s cc bn sao cn thit i vi mt thnh vin tt

nht. Vi lng bin tng i nh (n = 50 n 100)

2: Xt trong mi quan h gia mng nron v gii thut di truynCc ton t lai ghp, t bin, chn lc v hm thch nghi c p dng mt

cch c th

Mt c th bao gm cc thng tin: cc hm thuc hnh tam gic v cc gi trthc.



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2.1 Cross-over (Lai ghp)

Ton t lai ghp ngha l thay i v tr nh ca hm thnh vin th. Cch

thc lm vic ca ton t ny th hin trong hnh 4. C hai chui (cha, m)

trong mt mu c la chn bt k. S dng cc chui la chn ny, im

lai ghp cng c la chn gia cc gi tr xy dng nn v tr nh ca



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hm thnh vin. Cu trc c thay i gia mi chui da trn im lai,

cc kt qu, chng phi tri qua ton t lai to ra 4 kiu.

(1)cc gi tr bn phi ca chui c k tha t mt chui (A) v cc

gi tr bn cnh c k tha t mt chui khc (B), trong mi gi

tr l bn sao bi tng cc gi tr ca mt chui.

(2)Cc gi tr bn phi ca chui c k tha t chui (B) v cc gi

tr bn cnh c k tha t chui (A), trong mi gi tr l bn sao

ca tng cc gi tr ca chui.

(3)Cng ging nh cch thc trn, cc gi tr bn tri ca chui c k

tha t chui (A).

(4)cc gi tr bn tri ca chui c k tha t chui (B).

Nh th hin hnh 4, nh ca hm thnh vin c thay i i vimi kt qu. Kt qu c th k tha thng tin di truyn mc cao hn t hai

cha m bi ton t lai ghp. Cc gi tr th hai v th ba trong thao tc m

ho c tha k m khng thay i.

2.2 Mutation (t bin)Ton t t bin xut hin i vi cc chui, chng tri qua ton t t bin,

vi xc sut Pm. Ton t t bin trong ny c ngha l hm thnh vin la

chn c ct bt, c th hin trong hnh 5. Chng ta c th mong rng

lm gim bt s lut m v thu c cu trc nh nht ca m hnh m bi

thao tc ny.

Theo cch ny, mt chui mi c sinh ra bng cc thao tc lai ghp v t

bin. Cc thao tc ny nhn a ra mt cu trc thch hp ca m hnh m,

tng ng vi qu trnh iu chnh th.

2.3 Fitness function (Hm thch nghi)



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Chng ta t hm thch nghi nh gi mi chui liu c mong mun c

hay khng. Hm thch nghi ny bao gm s cc hm thnh vin v tng bnh

phng li gia gi tr ra ca m hnh m v gi tr mong mun. Chng ta

xc nh hm F ny nh sau:

F(si) = E + A*N

Trong , si l chui th i, E l tng bnh phng li gia gi tr ra ca m

hnh m v gi tr mong mun, A l hng s, N l s hm thnh vin. Chngta xc nh c rng chui no c thch nghi nh hn hay ln hn.



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2.4 Selection (chn lc)Da vo gi tr thch nghi, mi chui c la chn hay khng th h k

tip. Chng ta chp nhn duy tr chin lc l cc chui c thch nghi cao

tn ti trong th h k tip v chin lc ly mu rng cc chui c la

chn tn ti mt cch ngu nhin trong th h k tip. Cc chui tri qua cc

php ton di truyn nhiu c thch nghi cao, hn na chng s tip tc

tn ti trong th h k tip bng php chn lc. Php chn lc ny, chng

thay i mu ca cc chui, thc hin s tin ho.

3: Chin lc iu chnh m t ng

Thut ton iu chnh thu c m hnh m ti u. Th tc ny c th

hin hnh 6(1)Th h khi to c mt vi chui trong mi chui bao gm mt

tham s xy dng m hnh m c sinh ra mt cch ngu nhin

(2)Cc ton t t bin v lai ghp thc hin vic to cc chui mi.

Thut ton di truyn xc nh cu trc ca m hnh m th v s lut



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m nh nht. Cc ton t ny ph hp vi qu trnh iu chnh th,

nh cp trc

(3)S dng cc chui tri qua cc ton t di truyn, cc hm thnh vin

v cc gi tr thc trong mi chui c n khp vi lut delta [8].

Lut delta xc nh cu trc m hnh m tt.Thao tc ny ph hp vi

qu trnh iu chnh th

(4)Mi chui c nh gi trn c s mt hm thch nghi t c

bin php nh hng

(5)Da trn gi tr thch nghi, mi chui tri qua thao tc la chn, chng

thay i mu ca chui v thc hin s tin ho.

(6)Nu chng ta thu c chui mc tiu hoc tm kim t n th h

gii hn, s tm kim l qu rng; ni cch khc l quay tr li ton tdi truyn(2). Chng ta xc nh c chui mc tiu khi thch nghi

ca chui hi t trong li ch.

IV: KT LUN ti: Ti u ho cu trc ca mng nron l mt ti kh v ang trongqu trnh tip tc c nghin cu.

Mong c s ng gp ca thy c v cc bn ti c hon thin

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