Improving genetic algorithms' efficiency using intelligent fitness functions

Authors:
Jason Cooper;Chris Hinde
Affiliations:
Department of Computer Science, Loughborough University, Loughborough, UK;Department of Computer Science, Loughborough University, Loughborough, UK
Venue:
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Year:
2003

Citing 4
Cited 1

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Comparison Of Methods For Using Reduced Models To Speed Up Design Optimization

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Fitness Approximation In Evolutionary Computation - a Survey

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference

Feature subset selection in large dimensionality domains

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

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.