Binary and Gray Encoding in Univariate Marginal Distribution Algorithm, Genetic Algorithm, and Stochastic Hillclimbing

Cezary Janikow; Uday Chakraborty

Binary and Gray Encoding in Univariate Marginal Distribution Algorithm, Genetic Algorithm, and Stochastic Hillclimbing

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Abstract

This paper employs a Markov model to study the relative performance of binary and Gray coding in the univariate marginal distribution algorithm, genetic algorithm, and stochastic hillclimbing. The results indicate that while there is little difference between the two for all possible functions, Gray coding does not necessarily improve performance for functions which have fewer local optima in the Gray representation than in binary.

Original language	American English
Journal	Proceedings of the 7th International Conference on Genetic Algorithms
State	Published - Jul 19 1997

Disciplines

Computer Sciences

Cite this

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title = "Binary and Gray Encoding in Univariate Marginal Distribution Algorithm, Genetic Algorithm, and Stochastic Hillclimbing",

abstract = " This paper employs a Markov model to study the relative performance of binary and Gray coding in the univariate marginal distribution algorithm, genetic algorithm, and stochastic hillclimbing. The results indicate that while there is little difference between the two for all possible functions, Gray coding does not necessarily improve performance for functions which have fewer local optima in the Gray representation than in binary.",

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N2 - This paper employs a Markov model to study the relative performance of binary and Gray coding in the univariate marginal distribution algorithm, genetic algorithm, and stochastic hillclimbing. The results indicate that while there is little difference between the two for all possible functions, Gray coding does not necessarily improve performance for functions which have fewer local optima in the Gray representation than in binary.

AB - This paper employs a Markov model to study the relative performance of binary and Gray coding in the univariate marginal distribution algorithm, genetic algorithm, and stochastic hillclimbing. The results indicate that while there is little difference between the two for all possible functions, Gray coding does not necessarily improve performance for functions which have fewer local optima in the Gray representation than in binary.

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Binary and Gray Encoding in Univariate Marginal Distribution Algorithm, Genetic Algorithm, and Stochastic Hillclimbing

Abstract

Disciplines

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