Learning the dominance in diploid genetic algorithms for changing optimization problems

Date

2007

Advisors

Journal Title

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DOI

Volume Title

Publisher

China University of GeoSciences Press

Type

Conference

Peer reviewed

Yes

Abstract

Using diploid representation with dominance scheme is one of the approaches developed for genetic algorithms to address dynamic optimization problems. This paper proposes an adaptive dominance mechanism for diploid 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 learnt adaptively during the searching progress. The proposed dominance scheme is experimentally compared to two other schemes for diploid genetic algorithms. Experimental results validate the efficiency of the dominance learning scheme.

Description

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Citation

Yang, S. (2007) Learning the dominance in diploid genetic algorithms for changing optimization problems. Proceedings of the 2nd International Symposium on Intelligence Computation and Applications, pp. 157-162

Rights

Research Institute

Institute of Artificial Intelligence (IAI)