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Improving genetic algorithms' efficiency using intelligent fitness functions
conference contributionposted on 2013-08-05, 09:19 authored by Jason CooperJason 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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CitationCOOPER, 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
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NotesThis article was accepted for publication in the series Lecture Notes in Computer Science. The final publication is available at http://link.springer.com/
Book seriesNotes in Computer Science;2718