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

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4 Author(s)
da Cunha Brito, L. ; Escola de Eng. Eletr. e de Comput., Univ. Fed. de Goias, Goiania, Brazil ; Macedo, C.J.A. ; Rocha, A.S. ; de 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.

Published in:

Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on

Date of Conference:

23-28 Oct. 2010