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1、Whatisageneticalgorithm?lMethodsofrepresentationlMethodsofselectionlMethodsofchangelOtherproblem-solvingtechniquesConciselystated,ageneticalgorithm(orGAforshort)isaprogrammingtechniquethatmimicsbiologicalevolutionasaproblem-solvingstrategy.Givenaspecificproblem
2、tosolve,theinputtotheGAisasetofpotentialsolutionstothatproblem,encodedinsomefashion,andametriccalledafitnessfunctionthatallowseachcandidatetobequantitativelyevaluated.Thesecandidatesmaybesolutionsalreadyknowntowork,withtheaimoftheGAbeingtoimprovethem,butmoreoft
3、entheyaregeneratedatrandom.TheGAthenevaluateseachcandidateaccordingtothefitnessfunction.Inapoolofrandomlygeneratedcandidates,ofcourse,mostwillnotworkatall,andthesewillbedeleted.However,purelybychance,afewmayholdpromise-theymayshowactivity,evenifonlyweakandimper
4、fectactivity,towardsolvingtheproblem.Thesepromisingcandidatesarekeptandallowedtoreproduce.Multiplecopiesaremadeofthem,butthecopiesarenotperfect;randomchangesareintroducedduringthecopyingprocess.Thesedigitaloffspringthengoontothenextgeneration,forminganewpoolofc
5、andidatesolutions,andaresubjectedtoasecondroundoffitnessevaluation.Thosecandidatesolutionswhichwereworsened,ormadenobetter,bythechangestotheircodeareagaindeleted;butagain,purelybychance,therandomvariationsintroducedintothepopulationmayhaveimprovedsomeindividual
6、s,makingthemintobetter,morecompleteormoreefficientsolutionstotheproblemathand.Againthesewinningindividualsareselectedandcopiedoverintothenextgenerationwithrandomchanges,andtheprocessrepeats.Theexpectationisthattheaveragefitnessofthepopulationwillincreaseeachrou
7、nd,andsobyrepeatingthisprocessforhundredsorthousandsofrounds,verygoodsolutionstotheproblemcanbediscovered.Asastonishingandcounterintuitiveasitmayseemtosome,geneticalgorithmshaveproventobeanenormouslypowerfulandsuccessfulproblem-solvingstrategy,dramaticallydemon
8、stratingthepowerofevolutionaryprinciples.Geneticalgorithmshavebeenusedinawidevarietyoffieldstoevolvesolutionstoproblemsasdifficultasormoredifficultthanthosefacedbyhumandesig
