Article

Improving genetic algorithms' efficiency using intelligent fitness functions

Authors:

Jason Cooper,

Chris HindeAuthors Info & Claims

IEA/AIE'2003: Proceedings of the 16th international conference on Developments in applied artificial intelligence

Pages 636 - 643

Published: 23 June 2003 Publication History

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.

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Cited By

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Alsaeedan WMenai M(2015)A Self-adaptive Genetic Algorithm for the Word Sense Disambiguation ProblemProceedings of the 28th International Conference on Current Approaches in Applied Artificial Intelligence - Volume 910110.1007/978-3-319-19066-2_56(581-590)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1007/978-3-319-19066-2_56
Gheyas ISmith L(2010)Feature subset selection in large dimensionality domainsPattern Recognition10.1016/j.patcog.2009.06.00943:1(5-13)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.patcog.2009.06.009
Withall MHinde CStone R(2009)An improved representation for evolving programsGenetic Programming and Evolvable Machines10.1007/s10710-008-9069-710:1(37-70)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1007/s10710-008-9069-7

Index Terms

Improving genetic algorithms' efficiency using intelligent fitness functions
1. Mathematics of computing
  1. Discrete mathematics
    1. Combinatorics
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

IEA/AIE'2003: Proceedings of the 16th international conference on Developments in applied artificial intelligence

June 2003

814 pages

ISBN:3540404554

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Springer Springer Verlag Inc

Publication History

Published: 23 June 2003

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Alsaeedan WMenai M(2015)A Self-adaptive Genetic Algorithm for the Word Sense Disambiguation ProblemProceedings of the 28th International Conference on Current Approaches in Applied Artificial Intelligence - Volume 910110.1007/978-3-319-19066-2_56(581-590)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1007/978-3-319-19066-2_56
Gheyas ISmith L(2010)Feature subset selection in large dimensionality domainsPattern Recognition10.1016/j.patcog.2009.06.00943:1(5-13)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.patcog.2009.06.009
Withall MHinde CStone R(2009)An improved representation for evolving programsGenetic Programming and Evolvable Machines10.1007/s10710-008-9069-710:1(37-70)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1007/s10710-008-9069-7

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Neural network crossover in genetic algorithms using genetic programming

Using messy genetic algorithms for solving the winner determination problem

Optimisation of the weighting functions of an H ∞ controller using genetic algorithms and structured genetic algorithms

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