Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments.

Date

2011

Advisors

Journal Title

Journal ISSN

ISSN

1432-7643

Volume Title

Publisher

Springer-Verlag

Type

Article

Peer reviewed

Abstract

In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.

Description

Keywords

Artificial intelligence, Estimation of distribution algorithm, Dynamic optimization problem, Environment identification, Memory scheme, Diversity compensation

Citation

Peng, X., Gao, X. and Yang, S. (2011) Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments. Soft Computing, 15(2), February 2011, pp. 311-326.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)