完整word版外文翻译遗传算法Word下载.docx

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Conciselystated,ageneticalgorithm（orGAforshort）isaprogrammingtechniquethatmimicsbiologicalevolutionasaproblem-solvingstrategy.Givenaspecificproblemtosolve,theinputtotheGAisasetofpotentialsolutionstothatproblem,encodedinsomefashion,andametriccalledafitnessfunctionthatallowseachcandidatetobequantitativelyevaluated.Thesecandidatesmaybesolutionsalreadyknowntowork,withtheaimoftheGAbeingtoimprovethem,butmoreoftentheyaregeneratedatrandom.

TheGAthenevaluateseachcandidateaccordingtothefitnessfunction.Inapoolofrandomlygeneratedcandidates,ofcourse,mostwillnotworkatall,andthesewillbedeleted.However,purelybychance,afewmayholdpromise-theymayshowactivity,evenifonlyweakandimperfectactivity,towardsolvingtheproblem.

Thesepromisingcandidatesarekeptandallowedtoreproduce.Multiplecopiesaremadeofthem,butthecopiesarenotperfect;

randomchangesareintroducedduringthecopyingprocess.Thesedigitaloffspringthengoontothenextgeneration,forminganewpoolofcandidatesolutions,andaresubjectedtoasecondroundoffitnessevaluation.Thosecandidatesolutionswhichwereworsened,ormadenobetter,bythechangestotheircodeareagaindeleted;

butagain,purelybychance,therandomvariationsintroducedintothepopulationmayhaveimprovedsomeindividuals,makingthemintobetter,morecompleteormoreefficientsolutionstotheproblemathand.Againthesewinningindividualsareselectedandcopiedoverintothenextgenerationwithrandomchanges,andtheprocessrepeats.Theexpectationisthattheaveragefitnessofthepopulationwillincreaseeachround,andsobyrepeatingthisprocessforhundredsorthousandsofrounds,verygoodsolutionstotheproblemcanbediscovered.

Asastonishingandcounterintuitiveasitmayseemtosome,geneticalgorithmshaveproventobeanenormouslypowerfulandsuccessfulproblem-solvingstrategy,dramaticallydemonstratingthepowerofevolutionaryprinciples.Geneticalgorithmshavebeenusedinawidevarietyoffieldstoevolvesolutionstoproblemsasdifficultasormoredifficultthanthosefacedbyhumandesigners.Moreover,thesolutionstheycomeupwithareoftenmoreefficient,moreelegant,ormorecomplexthananythingcomparableahumanengineerwouldproduce.Insomecases,geneticalgorithmshavecomeupwithsolutionsthatbaffletheprogrammerswhowrotethealgorithmsinthefirstplace!

Methodsofrepresentation

Beforeageneticalgorithmcanbeputtoworkonanyproblem,amethodisneededtoencodepotentialsolutionstothatprobleminaformthatacomputercanprocess.Onecommonapproachistoencodesolutionsasbinarystrings:

sequencesof1'

sand0'

s,wherethedigitateachpositionrepresentsthevalueofsomeaspectofthesolution.Another,similarapproachistoencodesolutionsasarraysofintegersordecimalnumbers,witheachpositionagainrepresentingsomeparticularaspectofthesolution.Thisapproachallowsforgreaterprecisionandcomplexitythanthecomparativelyrestrictedmethodofusingbinarynumbersonlyandoften"

isintuitivelyclosertotheproblemspace"

（FlemingandPurshouse2002,p.1228）.

Thistechniquewasused,forexample,intheworkofSteffenSchulze-Kremer,whowroteageneticalgorithmtopredictthethree-dimensionalstructureofaproteinbasedonthesequenceofaminoacidsthatgointoit（Mitchell1996,p.62）.Schulze-Kremer'

sGAusedreal-valuednumberstorepresenttheso-called"

torsionangles"

betweenthepeptidebondsthatconnectaminoacids.（Aproteinismadeupofasequenceofbasicbuildingblockscalledaminoacids,whicharejoinedtogetherlikethelinksinachain.Oncealltheaminoacidsarelinked,theproteinfoldsupintoacomplexthree-dimensionalshapebasedonwhichaminoacidsattracteachotherandwhichonesrepeleachother.Theshapeofaproteindeterminesitsfunction.）Geneticalgorithmsfortrainingneuralnetworksoftenusethismethodofencodingalso.

AthirdapproachistorepresentindividualsinaGAasstringsofletters,whereeachletteragainstandsforaspecificaspectofthesolution.OneexampleofthistechniqueisHiroakiKitano'

s"

grammaticalencoding"

approach,whereaGAwasputtothetaskofevolvingasimplesetofrulescalledacontext-freegrammarthatwasinturnusedtogenerateneuralnetworksforavarietyofproblems（Mitchell1996,p.74）.

Thevirtueofallthreeofthesemethodsisthattheymakeiteasytodefineoperatorsthatcausetherandomchangesintheselectedcandidates:

flipa0toa1orviceversa,addorsubtractfromthevalueofanumberbyarandomlychosenamount,orchangeonelettertoanother.（SeethesectiononMethodsofchangeformoredetailaboutthegeneticoperators.）Anotherstrategy,developedprincipallybyJohnKozaofStanfordUniversityandcalledgeneticprogramming,representsprogramsasbranchingdatastructurescalledtrees（Kozaetal.2003,p.35）.Inthisapproach,randomchangescanbebroughtaboutbychangingtheoperatororalteringthevalueatagivennodeinthetree,orreplacingonesubtreewithanother.

Figure1:

Threesimpleprogramtreesofthekindnormallyusedingeneticprogramming.Themathematicalexpressionthateachonerepresentsisgivenunderneath.

Itisimportanttonotethatevolutionaryalgorithmsdonotneedtorepresentcandidatesolutionsasdatastringsoffixedlength.Somedorepresenttheminthisway,butothersdonot;

forexample,Kitano'

sgrammaticalencodingdiscussedabovecanbeefficientlyscaledtocreatelargeandcomplexneuralnetworks,andKoza'

sgeneticprogrammingtreescangrowarbitrarilylargeasnecessarytosolvewhateverproblemtheyareappliedto.

Methodsofselection

Therearemanydifferenttechniqueswhichageneticalgorithmcanusetoselecttheindividualstobecopiedoverintothenextgeneration,butlistedbelowaresomeofthemostcommonmethods.Someofthesemethodsaremutuallyexclusive,butotherscanbeandoftenareusedincombination.

Elitistselection:

Themostfitmembersofeachgenerationareguaranteedtobeselected.（MostGAsdonotusepureelitism,butinsteaduseamodifiedformwherethesinglebest,orafewofthebest,individualsfromeachgenerationarecopiedintothenextgenerationjustincasenothingbetterturnsup.）

Fitness-proportionateselection:

Morefitindividualsaremorelikely,butnotcertain,tobeselected.

Roulette-wheelselection:

Aformoffitness-proportionateselectioninwhichthechanceofanindividual'

sbeingselectedisproportionaltotheamountbywhichitsfitnessisgreaterorlessthanitscompetitors'

fitness.（Conceptually,thiscanberepresentedasagameofroulette-eachindividualgetsasliceofthewheel,butmorefitonesgetlargerslicesthanlessfitones.Thewheelisthenspun,andwhicheverindividual"

owns"

thesectiononwhichitlandseachtimeischosen.）

Scalingselection:

Astheaveragefitnessofthepopulationincreases,thestrengthoftheselectivepressurealsoincreasesandthefitnessfunctionbecomesmorediscriminating.Thismethodcanbehelpfulinmakingthebestselectionlateronwhenallindividualshaverelativelyhighfitnessandonlysmalldifferencesinfitnessdistinguishonefromanother.

Tournamentselection:

Subgroupsofindividualsarechosenfromthelargerpopulation,andmembersofeachsubgroupcompeteagainsteachother.Onlyoneindividualfromeachsubgroupischosentoreproduce.

Rankselection:

Eachindividualinthepopulationisassignedanumericalrankbasedonfitness,andselectionisbasedonthisrankingratherthanabsolutedifferencesinfitness.Theadvantageofthismethodisthatitcanpreventveryfitindividualsfromgainingdominanceearlyattheexpenseoflessfitones,whichwouldreducethepopulation'

sgeneticdiversityandmighthinderattemptstofindanacceptablesolution.

Generationalselection:

Theoffspringoftheindividualsselectedfromeachgenerationbecometheentirenextgeneration.Noindividualsareretainedbetweengenerations.

Steady-stateselection:

Theoffspringoftheindividualsselectedfromeachgenerationgobackintothepre-existinggenepool,replacingsomeofthelessfitmembersofthepreviousgeneration.Someindividualsareretainedbetweengenerations.

Hierarchicalselection:

Individualsgothroughmultipleroundsofselectioneachgeneration.Lower-levelevaluationsarefasterandlessdiscriminating,whilethosethatsurvivetohigherlevelsareevaluatedmorerigorously.Theadvantageofthismethodisthatitreducesoverallcomputationtimebyusingfaster,lessselectiveevaluationtoweedoutthemajorityofindividualsthatshowlittleornopromise,andonlysubjectingthosewhosurvivethisinitialtesttomorerigorousandmorecomputationallyexpensivefitnessevaluation.

Methodsofchange

Onceselectionhaschosenfitindividuals,theymustberandomlyalteredinhopesofimprovingtheirfitnessforthenextgeneration.Therearetwobasicstrategiestoaccomplishthis.Thefirstandsimplestiscalledmutation.Justasmutationinlivingthingschangesonegenetoanother,somutationinageneticalgorithmcausessmallalterationsatsinglepointsinanindividual'

scode.

Thesecondmethodiscalledcrossover,andentailschoosingtwoindividualstoswapsegmentsoftheircode,producingartificial"

offspring"

thatarecombinationsoftheirparents.Thisprocessisintendedtosimulatetheanalogousprocessofrecombinationthatoccurstochromosomesduringsexualreproduction.Commonformsofcrossoverinclud