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Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization

Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented that is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln ( n ) where n is the number of parameters. Results up to n = 1000 are reported. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Evolutionary Computation MIT Press

Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization

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References (35)

Publisher
MIT Press
Copyright
© 1993 by the Massachusetts Institute of Technology
ISSN
1063-6560
eISSN
1530-9304
DOI
10.1162/evco.1993.1.1.25
Publisher site
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Abstract

In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented that is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln ( n ) where n is the number of parameters. Results up to n = 1000 are reported.

Journal

Evolutionary Computation – MIT Press

Published: Mar 1, 1993

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