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

da Cunha Brito, L.Macedo, C.J.A.Rocha, Carvalho, P.H.P.

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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