Adaptive primal–dual genetic algorithms in dynamic environments.

Date

2009

Advisors

Journal Title

Journal ISSN

ISSN

1083-4419

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

Abstract

Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.

Description

Keywords

Adaptive dominant replacement scheme, Lamarckian learning, Dynamic optimization problem (DOP), Genetic algorithm (GA), Primal–dual mapping (PDM)

Citation

Wang, H. et al. (2009) Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(6), December 2009, pp. 1348-1361.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)