Prof. LEUNG Kwong SakDuan, Qi-HongQi-HongDuanXu, Zong-BenZong-BenXuWong C.K.Wong C.K.Duan Q.-H.Xu Z.-B.2023-03-292023-03-292001IEEE Transactions on Evolutionary Computation, 2001, vol. 5 (1), pp. 3 - 161089778Xhttp://hdl.handle.net/20.500.11861/7660There have been various algorithms designed for simulating natural evolution. This paper proposes a new simulated evolutionary computation model called the abstract evolutionary algorithm (AEA), which unifies most of the currently known evolutionary algorithms and describes the evolution as an abstract stochastic process composed of two fundamental operators: selection and evolution operators. By axiomatically characterizing the properties of the fundamental selection and evolution operators, several general convergence theorems and convergence rate estimations for the AEA are established. The established theorems are applied to a series of known evolutionary algorithms, directly yielding new convergence conditions and convergence rate estimations of various specific genetic algorithms and evolutionary strategies. The present work provides a significant step toward the establishment of a unified theory of simulated evolutionary computation.enEvolutionary ComputationEvolutionary AlgorithmsSelection OperatorEvolutionary StrategyUnified TheoryConvergence ConditionEvolution OperatorFundamental OperationGeneral ConvergenceSelective PressureMutation RateGeneral ConditionFunction of TypeAnalysis AlgorithmFitness FunctionGlobal OptimizationFeasible SetModel In This PaperNoisy EnvironmentsSurvival of The FittestCrossover OperatorIndependent OperatorsImplementation of SchemeSelection IntensityGlobal ConvergencePopulation ProbabilityGlobal CommunicationPositive ProbabilityCombination of OperatorsPeer Reviewed Journal Article10.1109/4235.910461