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Improving genetic algorithms' efficiency using intelligent fitness functions

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conference contribution
posted on 05.08.2013 by Jason Cooper, Christopher Hinde
Genetic Algorithms are an effective way to solve optimisation problems. If the fitness test takes a long time to perform then the Genetic Algorithm may take a long time to execute. Using conventional fitness functions Approximately a third of the time may be spent testing individuals that have already been tested. Intelligent Fitness Functions can be applied to improve the efficiency of the Genetic Algorithm by reducing repeated tests. Three types of Intelligent Fitness Functions are introduced and compared against a standard fitness function The Intelligent Fitness Functions are shown to be more efficient.

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School

  • University Academic and Administrative Support

Department

  • University Library

Citation

COOPER, J.L. and HINDE, C.J., 2003. Improving genetic algorithms' efficiency using intelligent fitness functions. IN: Chung, P.W.H., Hinde, C. and Ali, M. (eds). Developments in Applied Artificial Intelligence: 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2003 Loughborough, UK, June 23–26, 2003 Proceedings. Lecture Notes in Computer Science; 2718, pp.636–643.

Publisher

© Springer-Verlag Berlin Heidelberg

Version

AM (Accepted Manuscript)

Publication date

2003

Notes

This article was accepted for publication in the series Lecture Notes in Computer Science. The final publication is available at http://link.springer.com/

ISBN

3540404554;9783540404552

Book series

Notes in Computer Science;2718

Language

en

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