外文翻译遗传算法.docx
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外文翻译遗传算法
Whatisageneticalgorithm?
●Methodsofrepresentation
●Methodsofselection
●Methodsofchange
●Otherproblem-solvingtechniques
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,andentailschoosingtwoindividualstosoftheircode,producingartificial"offspring"thatarecombinationsoftheirparents.Thisprocessisintendedtosimulatetheanalogousprocessofrecombinationthatoccurstochromosomesduringsexualreproduction.Commonformsofcrossoverincludesingle-pointcros