Population-based incremental learning with associative memory for dynamic environments.

Date

2008

Advisors

Journal Title

Journal ISSN

ISSN

1089-778X

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

Abstract

In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPs. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multipopulation, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator, a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multipopulation schemes for PBILs in different dynamic environments.

Description

Keywords

Associative memory scheme, Dynamic optimization problems (DOPs), Immune system-based genetic algorithm (ISGA), Memory-enhanced genetic algorithm, Multipopulation scheme, Population-based incremental learning (PBIL), Random immigrants

Citation

Yang, S. and Yao, X. (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12(5), October 2008, pp. 542-561.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)