Experimental study on population-based incremental learning algorithms for dynamic optimization problems

Date

2005-11-01

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

Abstract

Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBIL’s adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.

Description

Keywords

Population-based incremental learning, Dynamic optimization problem, Dual population-based incremental learning, Genetic algorithm, Central probability vector, Exclusive-or operator

Citation

Yang, S. and Yao, X. (2005) Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9 (11), pp. 815-834

Rights

Research Institute

Institute of Artificial Intelligence (IAI)