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Learning to be selective in genetic-algorithm-based design optimization

Published online by Cambridge University Press:  01 June 1999

KHALED RASHEED  and
HAYM HIRSH
KHALED RASHEED
Affiliation:
Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
HAYM HIRSH
Affiliation:
Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
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Abstract

In this paper we describe a method for improving genetic-algorithm-based optimization using search control. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly improve the efficiency and reliability of the GA optimizer.

Keywords

Genetic AlgorithmsDesign OptimizationMachine Learning
Type
Research Article
Information
AI EDAM , Volume 13 , Issue 3 , June 1999 , pp. 157 - 169
Copyright
© 1999 Cambridge University Press

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