A Performance Analysis of Mono and Multi-objective Evolutionary Algorithms Assisted by Meta-modeling

Leonardo da Cunha Brito,Ciro Jase A. Macedo,Adson Silva Rocha,Paulo H. P. de Carvalho

Neural Networks（2010）

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摘要

Evolutionary Algorithms can be inefficient in optimizing problems in which fitness evaluation of candidate solutions is computationally expensive. In this paper, single and multi-objective evolutionary methods assisted by meta-models are proposed and analyzed. Meta-models are used to identify promising regions of search space in order to save evaluations of objective-functions. The meta-models are produced using regularized Radial Basis Functions networks. The study in this work shows that the method assisted by meta-modeling accelerates the convergence of the evolutionary process in mono and multi-objectives optimizations.

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关键词

multi-objective evolutionary,promising region,candidate solution,evolutionary process,fitness evaluation,search space,multi-objectives optimizations,multi-objective evolutionary method,optimizing problem,regularized radial basis functions,evolutionary algorithms,performance analysis,optimization problem,convergence,meta model,radial basis function network,evolutionary algorithm,optimization,metamodeling,evolutionary computation,indexes,objective function,multi objective optimization

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