A multiobjective methodology for evaluating genetic operators

Author

Takahashi, Ricardo H C ; Vasconcelos, J.A. ; Ramírez, Jaime A. ; Krahenbuhl, L.

Author_Institution

Dept. of Math., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil

Volume

39

Issue

3

fYear

2003

fDate

5/1/2003 12:00:00 AM

Firstpage

1321

Lastpage

1324

Abstract

This paper is concerned with the problem of evaluating genetic algorithm (GA) operator combinations. Each GA operator, like crossover or mutation, can be implemented according to several different formulations. This paper shows that: 1) the performances of different operators are not independent and 2) different merit figures for measuring a GA performance are conflicting. In order to account for this problem structure, a multiobjective analysis methodology is proposed. This methodology is employed for the evaluation of a new crossover operator (real-biased crossover) that is shown to bring a performance enhancement. A GA that was found by the proposed methodology is applied in an electromagnetic (EM) benchmark problem.

Keywords

genetic algorithms; mathematical operators; crossover operator; electromagnetic structure; figure of merit; genetic algorithm; multiobjective optimization; mutation operator; real-biased crossover operator; Computational efficiency; Cost accounting; Genetic algorithms; Genetic mutations; Information analysis; Magnetic analysis; Mathematics; Performance analysis; Performance evaluation; Stochastic processes;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/TMAG.2003.810371

Filename

1198464