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

Uday K. Chakraborty

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

University of Missouri-St. Louis

Research output: Contribution to conference › Presentation

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
State	Published - Jul 16 2003
Event	Workshop on Analysis and Design of Representations and Operators, International Conference on Genetic and Evolutionary Computation - Duration: Jul 16 2003 → …

Conference

Conference	Workshop on Analysis and Design of Representations and Operators, International Conference on Genetic and Evolutionary Computation
Period	7/16/03 → …

Disciplines

Computer Sciences

Cite this

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title = "Binary and Gray Enocding 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. ",

author = "Chakraborty, \{Uday K.\}",

note = "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.; Workshop on Analysis and Design of Representations and Operators, International Conference on Genetic and Evolutionary Computation ; Conference date: 16-07-2003",

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N1 - 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.

PY - 2003/7/16

Y1 - 2003/7/16

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.

UR - https://www.semanticscholar.org/paper/Binary-and-Gray-Encoding-in-Univariate-Marginal-%2C-%2C-Chakraborty/da1d3556a3cef22181b515dc4347f48fae060934

M3 - Presentation

T2 - Workshop on Analysis and Design of Representations and Operators, International Conference on Genetic and Evolutionary Computation

Y2 - 16 July 2003

ER -

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

Abstract

Conference

Disciplines

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Cite this