A genetic learning strategy in constrained search spaces

Author

Kommu, Venkataramana ; Pomeranz, Irith ; Abdelrahman, Tarek

Author_Institution

Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA

Volume

iii

fYear

1992

fDate

7-10 Jan 1992

Firstpage

26

Abstract

The performance of an adaptive learning algorithm based on evolution (the genetic algorithm) is investigated in constrained boolean search spaces where some solutions may be infeasible. This paper describes a randomized validation procedure to limit the genetic search to feasible regions of the search space. Analysis of the effect of the validation procedure on genetic optimization is presented. The performance of the modified genetic search on the set covering problem is used to illustrate the usefulness of the analysis in selecting the algorithm´s parameters

Keywords

Boolean functions; genetic algorithms; learning systems; search problems; adaptive learning algorithm; constrained boolean search spaces; constrained search spaces; genetic learning strategy; genetic optimization; performance; randomized validation procedure; Algorithm design and analysis; Circuits; Cities and towns; Constraint optimization; Genetic algorithms; Guidelines; Partitioning algorithms; Performance analysis; Routing;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences, 1992. Proceedings of the Twenty-Fifth Hawaii International Conference on

Conference_Location

Kauai, HI

Print_ISBN

0-8186-2420-5

Type

conf

DOI

10.1109/HICSS.1992.183462

Filename

183462