The compact genetic algorithm

Author

Harik, Georges R. ; Lobo, Fernando G. ; Goldberg, David E.

Author_Institution

Dept. of Gen. Eng., Illinois Univ., Urbana, IL, USA

Volume

3

Issue

4

fYear

1999

fDate

11/1/1999 12:00:00 AM

Firstpage

287

Lastpage

297

Abstract

Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of the GA´s parameters and operators. The paper clearly illustrates the mapping of the simple GA´s parameters into those of an equivalent compact GA. Computer simulations compare both algorithms in terms of solution quality and speed. Finally, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GAs

Keywords

genetic algorithms; probability; compact genetic algorithm; order-one behavior; probability distribution; uniform crossover; Algorithm design and analysis; Computational modeling; Computer simulation; Convergence; Genetic algorithms; Genetic engineering; History; Laboratories; Mathematical model; Probability distribution;

fLanguage

English

Journal_Title

Evolutionary Computation, IEEE Transactions on

Publisher

ieee

ISSN

1089-778X

Type

jour

DOI

10.1109/4235.797971

Filename

797971