Dominance learning in diploid genetic algorithms for dynamic optimization problems

Date

2006

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

ACM Press

Type

Conference

Peer reviewed

Yes

Abstract

This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance probability, which is learned adaptively during the searching progress and hence is adapted to the dynamic environment. Using a series of dynamic test problems, the proposed dominance scheme is compared to two other dominance schemes for diploidy genetic algorithms. The experimental results validate the efficiency of the proposed dominance learning scheme.

Description

Keywords

Diploid genetic algorithms, Dominance change scheme, Dominance learning, Dynamic optimization problems

Citation

Yang, S. (2006) Dominance learning in diploid genetic algorithms for dynamic optimization problems. GECCO'06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1435-1436

Rights

Research Institute

Institute of Artificial Intelligence (IAI)