Memetic algorithm using multi-surrogates for computationally expensive optimization problems

In this paper, we present a multi-surrogates assisted memetic algorithm for solving optimization problems with computationally expensive fitness functions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogate in the spir...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Germany), 2007-08, Vol.11 (10), p.957-971
Hauptverfasser:	Zhou, Zongzhao, Ong, Yew Soon, Lim, Meng Hiot, Lee, Bu Sung
Format:	Artikel
Sprache:	eng
Schlagworte:	Evolutionary algorithms Genetic algorithms Optimization
Online-Zugang:	Volltext
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