EC Applications.pptx

7/26/2019 EC Applications.pptx

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Evolutionary Computation and

Its Applications

Dr. K.INDIRAPrincipal

E.S. Engg. CollegeVillupuram

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Evolutionary Computation is te !eld o" studydevoted to te design# development and analysis"or solving pro$lem $ased on natural selection%simulated evolution&

Evolution as proven to $e a po'er"ul searcprocess

Evolutionary Computation as $een success"ully

applied to a 'ide range o" pro$lems including( Aircra"t Design# Routing in Communications Net'or)s#

*rac)ing +indsear#

,ame Playing %Cec)ers -ogel/&

INTRODUCTION


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0

Ro$otics#

Air *ra1c Control#

Design# Sceduling#

acine 3earning#

Pattern Recognition#

4o$ Sop Sceduling#

V3SI Circuit 3ayout#

Stri)e orce Allocation#

APPLICATIONS AREAS


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*eme Par) *ours %Disney 3and6+orld& ar)et orecasting#

Egg Price orecasting# Design o" ilters and 7arriers# Data8ining# 9ser8ining#

Resource Allocation# Pat Planning# Etc.

APPLICATIONS AREAS


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DATA MINING

Extraction of interesting information or

patterns from data in arge data!ases is "no#n

as data mining$

:


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ASSOCIATION RULE MINING

Association r%e mining finds interesting

associations and&or correation reations'ipsamong arge set of data items$

;


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GENETIC ALGORIT2M

PJP93A*IJN SE3EC*IJN

9*A*IJN CRJSSJVER 2B


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>o#c'art of ARM %sing GA

2=


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A pro$lem to solve# and ... Encoding tecniue %gene,

chromosome&

InitialiLation procedure(creation)

Evaluation "unction(environment)

Selection o" parents(reproduction)

,enetic operators (mutation,recombination)

GA COMPONENTS

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initialiLe populationG

evaluate populationG

'ile *ermination Criteria Not Satis!ed

select parents "or reproductionG

per"orm recom$ination andmutationG

evaluate populationGF

F

SIMPLE GA

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reproduction

population evaluation

modifcation

discard

deleted

members

parents

children

modified

children

evaluated children

GA C*CLE O> REPRODUCTION

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Cromosomes could $e(

7it strings%B=B= ... ==BB&

Real num$ers %50.2 800.= ...B.B >.2&

Permutations o" element %E== E0 E< ...E= E=:&

3ists o" rules %R= R2 R0 ...R22 R20&

Program elements %genetic

population

POPULATION

2:


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reproduction

population

parents

children

Parents are selected at random

'it selection cances $iased in relation to

cromosome evaluations.

REPRODUCTION

2;


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Random Selection

*ournament Selection

Ran) $ased Selection

Rolette +eel Selection

SELECTION T*PES

2ICATION

2


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Beore! "1 # 1 1 # 1 1 #$

Ater! "# 1 1 # # 1 1 #$

Beore! "1%&' ()*%+ &,)%++#%1$

Ater! "1%&' ()-%. &,)%++

#%1$ Causes movement in te searc space%local or glo$al& Restores lost in"ormation to te population

MUTATION

2>


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Crossover is a critical feature of genetic

algorithms: It greatly accelerates search early

in evolution of a population

CROSSO)ER

0B


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Jne8Point Crossover

*'o8Point Crossover

9ni"orm Crossover

CROSSO)ER T*PES

0=


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=8point e?ample(Parent=( =# 0# 5# 0# ;# =# 0# ;#


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9ni"orm crossover e?ample(

Parent=( =# 0# 5# 0# ;# =# 0# ;#


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*e evaluator decodes a cromosome

and assigns it a !tness measure *e evaluator is te only lin) $et'een

a classical ,A and te pro$lem it issolving

evaluation

evaluated

children

modified

children

E)ALUATION

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"enerational,A(entire populations replaced 'it eac

iteration #teady$state,A(

a "e' mem$ers replaced eac

generation

population

discard

discarded members

DELETION

0:


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RESEARC2 DIRECTIONS

Parameter $uning in GA

GA with ElitismA%aptive GA

&ocal Search

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atingPool

Selection Crossover

utationNe'

Solutions

Elitism

ElitePopulation

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9pdation o" velocity

o" particle in eacIteration

(Particle

8 7estparticle o" tes'arm

PARTCILE S?ARM OPTIMI4ATION

,eneratio

n =

,eneration 2

*arget%Solution&

,eneration N

5B


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Introductionto t/e PSO! Algoritm

=. Create a Mpopulation o" agents %particles&uni"ormly distri$uted over H

2. Evaluate eac particles position according

to te o$Oective "unction0. I" a particles current position is $etter

tan its previous $est position# update it

5. Determine te $est particle %according tote particles previous $est positions&

5=


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Introduction to t/e PSO! Algoritm

:. 9pdate particles velocities(

;. ove particles to teir ne' positions(


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Velocity 9pdationin PSJ

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PSO STATES

Exporation Expoitation

Particle

7estParticle o"

S'arm 55


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PSO STATES

Con+ergence @%mping O%t

5:


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Introduction to t/e PSO! Algoritm8 E?ample

5;


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5<


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5


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5>

d i / l i


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:B

d i / SO l i


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:=

I d i / PSO Al i


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:2

I t d ti t t/ PSO Al it


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:0

I t d ti t t/ PSO Al it


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Introduction to t/e PSO! AlgoritmCaracteristics

Advantages

Simple implementation

Easily paralleliLed "or concurrent processing

Very "e' algoritm parameters

Very e1cient glo$al searc algoritm

Disadvantages

*endency to a "ast and prematureconvergence in mid

optimum points

Slo' convergence in re!ned searc stage :5


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>o# c'art depicting t'e Genera PSO Agorit'm(

For each particles position (p)evaluate fitness

If fitness(p) better than

fitness(pbest) then pbest= pLoop

untilall

particlesexhaust

Set best of pBests as gBest

Upate particles velocit! an

position

"oopun

til#a$iter

Start

Initiali%e particles &ith rano# position

an velocit! vectors'

Stopgiving gBest opti#al solution'

>LO?C2ART O> PSO

::

RESEARC2 DIRECTIONS


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RESEARC2 DIRECTIONS

'ntro%uction of chaotic (aps

)eigh*orhoo% selection in PSO

A%aptive PSO +non %ata %epen%ent,

Data %epen%ent a%aptation in PSO

(emetic PSO with Shuffle% -rog &eaping Algorithm

.uantum Behave% PSO for A/(

0y*ri%ization of GA an% PSO

:;


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C2AOTIC PSO

T'e ne# c'aotic map mode is form%ated as

Met'odoog,

Initia point u0and v0to 0.1

T'e +eocit, of eac' partice is %pdated !,

:ind t'e nearest m partices as t'e neig'!or of t'e

c%rrent partice !ased on distance cac%ated C'oose t'e oca optim%m !est among t'e

neig'!or'ood in terms of fitness +a%es

Neig'!or'ood Seection

Met'odoog,

:


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SAPSO Non Data Dependent

T'e Inertia ?eig't in t'e +eocit, %pdate e-%ation is made

adapti+e$SAPSO: (

SAPSO5 (

SACPSO (

#'ere1 g is t'e generation index and G is a redefined maxim%mn%m!er of generations$ 2ere1 t'e maxima and minima #eig'ts maxand

minare set to 8$= and 8$61 !ased on experimenta st%d,$

:>


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Adapti+e PSO

Estimation of E+o%tionar, State done %sing distance

meas%re diand estimator e

Cassif, into #'ic' state partice !eongs and adapt t'eacceeration coefficients and Inertia ?eig't

Exporation

Expoitation Con+ergence @%mping O%t

;B


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APSO

Adapt t'e acceeration coefficients as gi+en in ta!e

;=

State Acceleration Coe1cient

c= c2

E?ploration Increase $y Decrease $y

E?ploitation Increase $y Q Decrease $y Q

Convergence Increase $y Q Increase $y Q

4umping out Decrease $y Increase $y


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MINING AR USING APSO

;2

T'e Inertia ?eig't is ad%sted as gi+en in e-%ation


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01B/'D GA2 PSO +GPSO, (ODE&

Genetic Algorithm Particle SwarmOptimization

Ad+antages

Go!a Optimi.ation Con+erges Easi,

GA #or"s on apopulationofpossi!e so%tion

PSO 'a+e noo+erapping andm%tationcac%ation

t'e, do not tend to!e easi, trapped!, oca optima

Memor,

Disad+antages

Cannot ass%reconstantoptimisationresponse times

T'e met'od easi,s%ffers from t'epartia optimism

M%tation andCrosso+er at timescreates c'idrenfara#a, from goodso%tions

?ea" oca searc'a!iit,

;0


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Genetic

Agorit'm

ParticeS#arm

Optimi.ation

E+a%ate>itness

Upper

Lo#er

InitiaPop%ation

Ran"edPop%ation

UpdatedPop%ation

;5



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? 8 copy%?$est&

or = to Elite

? 8 Select an Individual? 8 9pdate Velocity? 8 9pdate Position

?= 8 Select an Individual?2 8 Select an Individual

Crossover%?=# ?2&

utate%?=# ?2&

or = to %popsiLe8Elite& T $reedRatio

or = to %popsiLe8Elite&T%=8

7reedRatio

;:


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Mining AR %sing PSO S>LA

S'%ffed >rog Leaping Agorit'm /S>LA0 is adopted toperform t'e oca searc'

2ere t'e partices are ao#ed to gain some

experience1 t'ro%g' a oca searc'1 !efore !eingin+o+ed in t'e e+o%tionar, process

T'e s'%ffing process ao#s t'e partices to gain

information a!o%t t'e go!a !est$

;;


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3J+CUAR* JR PSOS>LA

Generation of initial population(P) and evaluating

the fitness of each particle

Velocity and position updation of particles

Distribution of frog into M memeplexes

Iterative pdating of !orst frog in each

memeplexes

"ombining all frogs to form a ne! population

#ermination

criteria satisfied$

Determine the best solution

%orting the population in descending order in

terms of fitness value

#%&'

;<

SH!!LE" !#$% LEAP&'%


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SH!!LE" !#$% LEAP&'%

AL%$#&THM (S!LA)

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0ro 1

0ro ,

0ro &

0ro -

0ro )

0ro .

0ro +

emeple*

emeple+

emeple

0ro '

Sortedrogs

ormation o" emeple?es

d f


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! F Position of t'e gro%p !est &go!a !est

# F Position of t'e #orst frog in t'e gro%pD

iF Cac%ated ne# position of t'e #orst frog

T'e position of t'e partices #it' #orst fitness ismodified %sing

Updation of ?orst Partices


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UANTUM HE2A)ED PSO

ERENCES

UANTUM HE2A)ED PSO


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IN>ERENCES


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PSO Met'odoog,

T'e partices mo+ement is !,(

?'ere1p J /c K pid /:Fc0 K pgd0

c J /c: K r:0& /c:Kr: c5Kr50

is t'e contractionFexpansion coefficient 81:

PSO >LO?C2ART


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YES

PSO >LO?C2ART


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es @ing Li1 2an R%iFfeng1 A SefFAdapti+e Genetic Agorit'm Hased OnReaF Coded1 Internationa Conference on Hiomedica Engineering and

comp%ter Science 1 Page/s0( : F 6 1 58:8

C'%anFQang Ting1 ?eiFMing 4eng1 T.%F C'ie' Lin1 Lin"age Disco+er,t'ro%g' Data Mining1 IEEE Maga.ine on Comp%tationa Inteigence1)o%me 1 >e!r%ar, 58:8$

Caises1 *$1 Le,+a1 E$1 Gon.ae.1 A$1 Pere.1 R$1 An extension of t'eGenetic Iterati+e Approac' for Learning R%e S%!sets 1 6t'Internationa ?or"s'op on Genetic and E+o%tionar, >%.., S,stems1Page/s0( 97 F 9; 1 58:8

S'angping Dai1 Li Gao1 iang 4'%1 C'ang#% 4'%1 A No+e Genetic

Agorit'm Hased on Image Data!ases for Mining Association R%es1 9t'IEEE&ACIS Internationa Conference on Comp%ter and InformationScience1 Page/s0( =;; =


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ansoori# E.,.# olgadri# .4.# Kate$i# S.D.# WS,ERD(A Steady8State ,enetic Algoritm "or E?tracting uLLyClassi!cation Rules rom DataX# IEEE *ransactions onuLLy Systems# Volume( =; # Issue( 5 # Page%s&( =B;= Y=B# Volume( = # Page%s&( 5 Y :2#2BB>

Uong ,uo# Za ou# WAn Algoritm "or iningAssociation Rules 7ased on Improved ,eneticAlgoritm and its ApplicationX# 0rd InternationalCon"erence on ,enetic and Evolutionary Computing#+,EC [B># Page%s&( ==< Y =2B# 2BB>

,en?ian an Uaisan Cen WImmune

Contd..

-)

Re"erencesC d


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Caises# Z.# 3eyva# E.# ,onLaleL# A.# PereL# R.# WAne?tension o" te ,enetic Iterative Approac "or3earning Rule Su$sets W# 5t International +or)sop

on ,enetic and Evolutionary uLLy Systems# Page%s&(;0 8 ;< # 2B=B

Hiaoyuan u# Zonguan Zu# Hueyan ,uo# W,eneticAlgoritm 7ased on Evolution Strategy and teApplication in Data iningX# irst International+or)sop on Education *ecnology and ComputerScience# E*CS [B># Volume( = # Page%s&( 5 Y :2# 2BB>

iguel RodrigueL# Diego . Escalante# AntonioPeregrin# E1cient Distri$uted ,enetic Algoritm "or

Rule e?traction# Applied So"t Computing == %2B==&


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*an)Zou

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EC Applications.pptx