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C H A P T E R 1
INTRODUCTION
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LECTURER
Dr Zeratul Izzah Mohd Yusoh
Industrial Computing
Leel !" #I $ing
%& ''1 &(%%
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Positioning o) EC and the *asi+ EC metaphor
Histori+al perspe+tie
,iologi+al inspiration-
Dar$inian eolution theor. /simpli)ied0
2eneti+s /simpli)ied0
Motiation )or EC
3hat +an EC do- e4amples o) appli+ation areas
CONTENTS
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Borg Vogons
Biotop
Art
Life Sciences Social Sciences
Mathematics Physics
Software Engineering
Neural Nets Evolutionary Computing Fuy Systems
Computational !ntelligence etc
Computer Science etc
E"act Sciences etc
Science Politics Sports etc
Society Stones # Seas etc
Earth etc
$niverse
%ou are here
POSITIONING OF EC
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POSITIONING OF EC
EC is part o) +omputer s+ien+e
EC is not part o) li)e s+ien+es5*iolog.
,iolog. deliered inspiration and terminolog.
EC +an *e applied in *iologi+al resear+h
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POSITIONING OF EA
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SUBCATEGORIES OF EA
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THE MAIN EVOLUTIONARY COMPUTINGMETAPHOR
EVOLUTION
Enironment
Indiidual
6itness
PROBLEM SOLVING
Pro*lem
Candidate 7olution
8ualit.
Quality
chance for seeding new solutions
Fitness chances for survival and reproduction
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BRIEF HISTORY 1: THE ANCESTORS
9 1:;
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BRIEF HISTORY 2: THE RISE OF EC
1985: first international conference (I!"#
199$: first international conference in %urope (&&'#
199): first scientific % *ournal (+I, &ress#
199-: launch of %uropean % .esearch etwor/ %voet
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DARWINIAN EVOLUTION 1:SURVIVAL OF THE FITTEST
All enironments hae )inite resour+es/i@e@" +an onl. support a limited num*er o) indiiduals
Li)e)orms hae *asi+ instin+t5 li)e+.+les geared to$ards
reprodu+tion There)ore some Bind o) sele+tion is ineita*le
Those indiiduals that +ompete )or the resour+es most
e))e+tiel. hae in+reased +han+e o) reprodu+tion ote- )itness in natural eolution is a deried" se+ondar.
measure" i@e@" $e /humans assign a high )itness to
indiiduals $ith man. o))spring
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DARWINIAN EVOLUTION:SUMMARY
Population +onsists o) dierse set o) indiiduals
Com*inations o) traits that are *etter adapted tendto in+rease representation in population
Indiiduals are =units o) sele+tion>
ariations o++ur through random +hanges .ielding+onstant sour+e o) diersit." +oupled $ith sele+tion
means that-
Population is the =unit o) eolution> ote the a*sen+e o) =guiding )or+e>
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ADAPTIVE LANDSCAPE METAPHOR(WRIGHT, 1932)
9 Can enisage population $ith n traits as e4isting in
a n+1dimensional spa+e /lands+ape $ith height+orresponding to )itness
9 Ea+h di))erent indiidual /phenot.pe represents asingle point on the lands+ape
9 Population is there)ore a =+loud> o) points" moingon the lands+ape oer time as it eoles adaptation
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EXAMPLE WITH TWO TRAITS
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NATURAL GENETICS
The in)ormation reFuired to *uild a liing organism is
+oded in the DA o) that organism 2enot.pe /DA inside determines phenot.pe
2enes phenot.pi+ traits is a +omple4 mapping
ne gene ma. a))e+t man. traits /pleiotrop. Man. genes ma. a))e+t one trait /pol.gen.
7mall +hanges in the genot.pe lead to small+hanges in the organism /e@g@" height" hair +olour
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GENES AND THE GENOME
2enes are en+oded in strands o) DA +alled
+hromosomes
In most +ells" there are t$o +opies o) ea+h
+hromosome /diploid.
The +omplete geneti+ material in an indiidualGsgenot.pe is +alled the 2enome
3ithin a spe+ies" most o) the geneti+ material is the
same
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EXAMPLE: HOMO SAPIENS
Human DA is organised into +hromosomes
Human *od. +ells +ontains !' pairs o)+hromosomes $hi+h together de)ine the ph.si+alattri*utes o) the indiidual-
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REPRODUCTIVE CELLS
2ametes /sperm and egg +ells +ontain !'
indiidual +hromosomes rather than !' pairs Cells $ith onl. one +op. o) ea+h +hromosome are
+alled Haploid
2ametes are )ormed *. a spe+ial )orm o) +ellsplitting +alled meiosis
During meiosis the pairs o) +hromosome undergo anoperation +alled crossing-over
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CROSSINGOVER DURING MEIOSIS
Chromosome pairs align and dupli+ate Inner pairs linB at a centromere and s$ap partso) themseles
0utcoe is one copy of aternal2paternalchroosoe plus two entirely new co3inations
"fter crossing4over one of each pair goes into each
gaete
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FERTILISATION
'per cell fro Father %gg cell fro +other
ew person cell (ygote#
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AFTER FERTILISATION
e$ z.gote rapidl. diides et+ +reating man. +ells
all $ith the same geneti+ +ontents Although all +ells +ontain the same genes"
depending on" )or e4ample $here the. are in the
organism" the. $ill *ehae di))erentl. This pro+ess o) di))erential *ehaiour during
deelopment is +alled ontogenesis
All o) this uses" and is +ontrolled *." the same
me+hanism )or de+oding the genes in DA
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MUTATION
++asionall. some o) the geneti+ material +hanges
er. slightl. during this pro+ess /repli+ation error This means that the +hild might hae geneti+
material in)ormation not inherited )rom either parent
This +an *e +atastrophi+- o))spring in not ia*le /most liBel.
neutral- ne$ )eature not in)luen+es )itness
adantageous- strong ne$ )eature o++urs
Redundan+. in the geneti+ +ode )orms a good$a. o) error +he+Bing
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MOTIVATIONS FOR EC: 1
ature has al$a.s sered as a sour+e o)
inspiration )or engineers and s+ientists
The *est pro*lem soler Bno$n in nature is-
the /human *rain that +reated =the $heel" e$ YorB"
$ars and so on> /a)ter Douglas AdamsG Hit+hHiBers2uide
the eolution me+hanism that +reated the human *rain/a)ter Dar$inGs rigin o) 7pe+ies
Ans$er 1 neuro+omputing Ans$er ! eolutionar. +omputing
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PROBLEM TYPE 1 : OPTIMISATION
3e hae a model o) our s.stem and seeB inputsthat gie us a spe+i)ied goal
e6g6
7 tie ta3les for university call center or hospital
7 design specifications etc etc
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0ptiisation eaple 1: niversity tieta3ling
%norously 3ig search space
,ieta3les ust 3e good
;!ood< is defined 3y a nu3er
of copeting criteria
,ieta3les ust 3e feasi3le
=ast a*ority of search space
is infeasi3le
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PROBLEM TYPES 2: MODELLING
3e hae +orresponding sets o) inputs outputsand seeB model that deliers +orre+t output )oreer. Bno$n input
%volutionary achine learning
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+odelling eaple: loan applicant crediti3ility
>ritish 3an/ evolvedcredita3ility odel to predict
loan paying 3ehavior of new
applicants
%volving: prediction odels
Fitness: odel accuracy onhistorical data
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PROBLEM TYPE 3: SIMULATION
3e hae a gien model and $ish to Bno$ theoutputs that arise under di))erent input +onditions
0ften used to answer ;what4if< ?uestions in evolving
dynaic environents
e6g6 %volutionary econoics "rtificial @ife
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SIMULATION EXAMPLE:EVOLVING ARTIFICIAL SOCIETIES
7imulating trade" e+onomi++ompetition" et+@ to +ali*ratemodels
se models to optimisestrategies and poli+ies
Eolutionar. e+onom.
7urial o) the )ittest isuniersal /*ig5small )ish