A Hybrid Evolutionary Algorithm for Global Optimization

Mend-Amar Majig (), Abdel-Rahman Hedar () and Masao Fukushima ()
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Mend-Amar Majig: Kyoto University
Abdel-Rahman Hedar: Kyoto University
Masao Fukushima: Kyoto University

A chapter in Optimization and Optimal Control, 2010, pp 169-184 from Springer

Abstract: Summary. In this work, we propose a method for finding as many as possible, hopefully all, solutions of the global optimization problem. For this purpose, we hybridize an evolutionary search algorithm with a fitness function modification procedure. Moreover, to make the method more effective, we employ some local search method and a special procedure to detect unpromising trial solutions. Numerical results for some well-known global optimization test problems show the method works well in practice.

Keywords: global optimization; tunneling function; evolutionary algorithm; local search (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-89496-6_9

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DOI: 10.1007/978-0-387-89496-6_9

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