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Convergence analysis of canonical genetic algorithms

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G. RudolphAuthors Info & Claims

IEEE Transactions on Neural Networks, Volume 5, Issue 1

Pages 96 - 101

https://doi.org/10.1109/72.265964

Published: 01 January 1994 Publication History

Abstract

This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional reproduction applied to static optimization problems. It is proved by means of homogeneous finite Markov chain analysis that a CGA will never converge to the global optimum regardless of the initialization, crossover, operator and objective function. But variants of CGA's that always maintain the best solution in the population, either before or after selection, are shown to converge to the global optimum due to the irreducibility property of the underlying original nonconvergent CGA. These results are discussed with respect to the schema theorem

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Convergence analysis of canonical genetic algorithms

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cover image IEEE Transactions on Neural Networks

IEEE Transactions on Neural Networks Volume 5, Issue 1

January 1994

145 pages

ISSN:1045-9227

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IEEE Press

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Published: 01 January 1994

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Doerr BLi XHandl J(2024)A Gentle Introduction to Theory (for Non-Theoreticians)Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3648402(800-829)Online publication date: 14-Jul-2024
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