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1、AnAnalysisofGenotype-PhenotypeMapsinGrammaticalEvolutionDavidFagan,MichaelO’Neill,EdgarGalv´an-L´opez,AnthonyBrabazon,andSeanMcGarraghyNaturalComputingResearch&ApplicationsGroupUniversityCollegeDublin,Ireland{david.fagan,m.oneill,edgar.galvan,anthony.brabazon,se
2、an.mcgarraghy}@ucd.ieAbstract.Wepresentananalysisofthegenotype-phenotypemapinGrammaticalEvolution(GE).ThestandardmapadoptedinGEisadepth-firstexpansionofthenon-terminalsymbolsduringthederivationsequence.Earlierstudieshaveindicatedthatallowingthepathoftheexpansiont
3、obeundertheguidanceofevolutionasopposedtoade-terministicprocessproducedsignificantperformancegainsonallofthebenchmarkproblemsanalysed.Inthisstudyweextendthisanalysistoin-cludeabreadth-firstandrandommap,investigateadditionalbenchmarkproblems,andtakeintoconsideratio
4、ntheimplicationsofrecentresultsonalternativegrammarrepresentationswiththisnewevidence.Wecon-cludethatitispossibletoimprovetheperformanceofgrammar-basedGeneticProgrammingbythemannerinwhichagenotype-phenotypemapisperformed.1IntroductionWithinthefieldofGeneticProgra
5、mming(GP)[11,19]theuseofagenotype-phenotypemapisnotnew[9,1,10,20,13,5,4,8]andanumberofvariantstothestandardtree-basedformofGPexist,amongstwhichsomeofthemostpopularareLinearGP[2],CartesianGP[14]andGrammaticalEvolution(GE)[3,18].GEisagrammar-basedformofGPwhichadop
6、tsamappingfromalineargenotypetophenotypicGPtrees.O’Neill[15]presentedaseriesofargumentsfortheadoptionofagenotype-phenotypemapforGPasitcanprovideanumberofadvantages.TheseincludeageneralisedencodingthatcanrepresentavarietyofstructuresallowingGPtogeneratestructures
7、inanarbitrarylanguage,efficiencygainsforevolutionarysearch(e.g.throughneutralevolution),maintenanceofgeneticdiversitythroughmany-to-onemaps,preservationoffunctionalitywhileallowingcontinuationofsearchatagenotypiclevel,reuseofgeneticmaterialpotentiallyallowinginfor
8、mationcompression,andpositionalindependenceofgenefunctionality.Forthefirsttimethisstudypresentsanexaminationofthegenotype-phenotypemapofGE.Anumberofalternativemappersa