Explicit memory schemes for evolutionary algorithms in dynamic environments

Date

2007-01

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Book chapter

Peer reviewed

Yes

Abstract

Problem optimization in dynamic environments has attracted a growing interest from the evolutionary computation community in recent years due to its importance in real world optimization problems. Several approaches have been developed to enhance the performance of evolutionary algorithms for dynamic optimization problems, of which the memory scheme is a major one. This chapter investigates the application of explicit memory schemes for evolutionary algorithms in dynamic environments. Two kinds of explicit memory schemes: direct memory and associative memory, are studied within two classes of evolutionary algorithms: genetic algorithms and univariate marginal distribution algorithms for dynamic optimization problems. Based on a series of systematically constructed dynamic test environments, experiments are carried out to investigate these explicit memory schemes and the performance of direct and associative memory schemes are compared and analysed. The experimental results show the efficiency of the memory schemes for evolutionary algorithms in dynamic environments, especially when the environment changes cyclically. The experimental results also indicate that the effect of the memory schemes depends not only on the dynamic problems and dynamic environments but also on the evolutionary algorithm used.

Description

Keywords

Dynamic optimization problems, explicit memory schemes, evolutionary algorithms

Citation

Yang, S. (2007) Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Yang, S., Ong, Y-S. and Jin, Y. (Eds.) Evolutionary Computation in Dynamic and Uncertain Environments, Volume 51, Berlin Heidelberg: Springer-Verlag, pp. 3-28.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)