精英引导的学习型遗传算法

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中图分类号：TP301.6 文献标识码：A文章编号：1006-8228（2025）11-46-06

Abstract：Toaddresstheissesoftraditionalgeneticalgorithmsincomplexotimizationproblems，suchaseasilyfallngntolcal optima，slowconvergence，andinsuicientpopulationdiversitythispaperproposesanelite-individual-guidedleainggenetic algorithm（LGA）Intheelitelearningphase，eliteindividulsguideospringindividulsbyfacilitatingadaptivelearingandgene retentionbasedonlearmingfactors.Intheadaptivemutationphase，individualsadjusttheirmutationrateadaptivelybasedontheir curentfitnessvalues，whileteintroductionofasuboptimalindividualguidedbydistancetoitdirectsindividualstoperfofine localsearches，therebyavoiding localoptima.Both phasesemployagreedyselectionstrategytoensurethepopulationdoesnot degradeinlaterevolutionarystages.Tovalidatethealgorithm'sperformance，comparativeexperimentswereconductedamongLGA， GA，andMGA.TheresultsdemonstratethatLGAsignificantlyoutperformsthecomparativealgorithmsintermsofconvergence speed，globalsearchcapabilitandstablitymetrics，fectivelybalancingthecoflictbetweenxploitationandexploatiand mitigating the inherent shortcomings of traditional genetic algorithms.

Keywords：Learning Genetic Algorithm; Elite Guidance;Adaptive Mutation;Adaptive Learning

0引言

遗传算法（GeneticAlgorithm，GA）受达尔文生物进化论启发-2，该算法融合了自然选择和遗传学的原理，从而实现对复杂优化问题的求解。（剩余5719字）