基于演化博弈论的协同进化算法的研究和应用的中期报告[Abstract]Cooperativecoevolutionaryalgorithms(CCEAs)havebeenwidelyusedinsolvingcomplexoptimizationproblems.However,mostexistingCCEAsarebasedonheuristics,lackingtheoreticalanalysisandrigorousproof.Evolutionarygametheory(EGT)providesamathematicalframeworkforstudyingtheevolutionofstrategiesinapopulationofindividuals.Inthisreport,weproposeanewapproachthatcombinesEGTandCCEAs,calledevolutionarygame-basedCCEA(EG-CCEA).WepresentthebasicconceptsandtheoreticalfoundationofEG-CCEA,anddemonstrateitsapplicationinsolvingabenchmarkoptimizationproblem.[Introduction]Cooperativecoevolutionaryalgorithms(CCEAs)havebeenwidelyusedinsolvingcomplexoptimizationproblems,suchasdesignoptimization,functionoptimization,andparametercalibration.ACCEAdecomposesacomplexproblemintosub-problems,whicharesolvedbydifferentsub-populationsofindividuals.Thesub-solutionsarethencombinedtoobtainthefinaloptimalsolution.However,mostexistingCCEAsarebasedonheuristics,lackingtheoreticalanalysisandrigorousproof.Therefore,itisdifficulttounderstandthebehaviorofCCEAsandtooptimizetheirparameters.Evolutionarygametheory(EGT)providesamathematicalframeworkforstudyingtheevolutionofstrategiesinapopulationofindividuals.EGThasbeensuccessfullyappliedinvariousfields,suchasbiology,economics,andsocialsciences.InEGT,thefitnessofastrategydependsonthestrategiesofotherindividualsinthepopulation,andthebeststrategyistheonethatachievesthehighestfitnessinthepopulation.EGTcanbeusedtoanalyzetheevolutionofcooperation,competition,andcoordinationinapopulationofindividuals.Inthisreport,weproposeanewapproachthatcombinesEGTandCCEAs,calledevolutionarygame-basedCCEA(EG-CCEA).EG-CCEAusesEGTtoguidetheevolutionofsub-populationsinCCEA,andcoordinatesthesub-solutionsbasedonthegame-theoreticalanalysis.WepresentthebasicconceptsandtheoreticalfoundationofEG-CCEA,anddemonstrateitsapplicationinsolvingabenchmarkoptimizationproblem.[Methodology]TheproposedEG-CCEAconsistsofthefollowingsteps:1.Decomposetheoriginalproblemintosub-prob