cooper-author-final-version.pdf (266.99 kB)
Download fileImproving genetic algorithms' efficiency using intelligent fitness functions
conference contribution
posted on 2013-08-05, 09:19 authored by Jason CooperJason Cooper, Christopher HindeGenetic 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.
History
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 HeidelbergVersion
- AM (Accepted Manuscript)
Publication date
2003Notes
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;9783540404552Publisher version
Book series
Notes in Computer Science;2718Language
- en