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Genetic Algorithms for Real Parameter Optimization
Genetic Algorithms for Real Parameter Optimization
Alden H. Wright
Department of Computer Science
University of Montana
Missoula, Montana 59812
wright@cs.umt.edu
Abstract
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This paper is concerned with the application of genetic
algorithms to optimization problems over several real
parameters. It is shown that k-point crossover (for k
small relative to the number of parameters) can be viewed
as a crossover operation on the vector of parameters plus
perturbations of some of the parameters. Mutation can also
be considered as a perturbation of some of the parameters.
This suggests a genetic algorithm that uses real parameter
vectors as chromosomes, real parameters as genes, and real
numbers as alleles. Such an algorithm is proposed with two
possible crossover methods. Schemata are defined for this
algorithm, and it is shown that Holland's Schema theorem
holds for one of these crossover methods. Experimental
results are given that indicate that this algorithm with a
mixture of the two crossover methods outperformed the
binary-coded genetic algorithm on 7 of 9 test problems.
Keywords: optimization, genetic algorithm, evolution
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Foundations of Genetic Algorithms, Edited by Gregory J. E. Rawlins,
Morgan Kaufman, 1991.