Reevaluating Genetic Algorithm Performance under coordinate Rotation of Benchmark Function

Reevaluating Genetic Algorithm Performance under coordinate Rotation of Benchmark Function

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时间:2019-07-11

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1、ReevaluatingGeneticAlgorithmPerformanceundercoordinateRotationofBenchmarkFunctionsAsurveyofsometheoreticalandpracticalaspectsofgeneticalgorithmsRalfSalomonAILab,ComputerScienceDepartment,UniversityofZurichWinterthurerstrasse190,8057Zurich,SwitzerlandFAX:+41-1-3630035;Emai

2、l:salomon@i .unizh.chAbstractThisworkanalyzessomeconceptsofgeneticalgorithmsandexplainswhytheymaybeappliedwithsuccesstosomeproblemsinfunctionoptimization.Inadditiontootherperformanceproperties,ithasbeenshownthatgeneticalgo-rithmsareabletoovercomelocalminimainhighlymultimo

3、dalfunctions(e.g.,Rastrigin,Schwefel).Theperformanceofgeneticalgorithmsissupportedbyanextensivetheory,whichisbasedontheassumptionofadditivegenee ects.Butthecurrentworkshowsthattheassumptionofadditivegenee ectsisnotweak,andthatthedependenceonspeci cparametersettingsismuchs

4、trongerthanoftenbelieved.Furthermore,theassumptionsregardingthe tnessfunctionaresorestrictingthatslightmodi cationsofthestandardtestfunctionscauseafailureoftheoptimizationprocedureeventhoughthefunction'sstructureispreserved.Thecurrentexperimentsfocusonafewwidely-usedscala

5、bletestfunctions.theresultsindicatethatastandardgeneticalgorithmcan ndtheglobaloptimumofRastrigin-likefunctionsnotduetotheeciencyofthere-combinationoperatorandtheregulardistributionoflocalminima,butbecausetheoptimizationisdecomposableintonindependentone-dimensionalsubpro

6、b-lems.Asalogicalconsequenceoftheanalysis,itisshownhowthefunction'spropertiescanbeexploitedinordertoconstructamoreecientalgorithm.KeywordsGENETICALGORITHMS{FUNCTIONOPTIMIZATION{PERFORMANCECOORDINATEROTATION11IntroductionAgeneticalgorithm(GA)isaheuristicsearchprocedurebas

7、edonnaturalselectionandgenetics,whichmaintainsapopulationoftrailsolutions.Geneticalgorithms(GAs)aresuitableforaddressingmanyfunctionoptimizationproblems.Recentde-velopmentssuchastheparallelgeneticalgorithm[10]andthebreedergeneticalgo-rithm[12,13]havebeenshowntobesuccessfu

8、linoptimizingmultimodalfunctions.IthasbeenreportedthatsuchGAsovercomemillionsoflocaloptimaandcan

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