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Title: Improving genetic algorithms' efficiency using intelligent fitness functions
Authors: Cooper, Jason
Hinde, Chris J.
Keywords: Genetic algorithms
Issue Date: 2003
Publisher: © Springer-Verlag Berlin Heidelberg
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.
Series/Report no.: Notes in Computer Science;2718
Abstract: 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.
Description: This article was accepted for publication in the series Lecture Notes in Computer Science. The final publication is available at http://link.springer.com/
Version: Accepted for publication
DOI: 9783540450344
URI: https://dspace.lboro.ac.uk/2134/12885
Publisher Link: http://dx.doi.org/10.1007/3-540-45034-3_64
ISBN: 3540404554
Appears in Collections:Published Articles (Library)
Conference Papers (Library)

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