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.