Adaptive primal–dual genetic algorithms in dynamic environments.

Date
2009
Authors
Wang, Hongfeng
Yang, Shengxiang
Ip, W. H.
Wang, Dingwei
Journal Title
Journal ISSN
ISSN
1083-4419
Volume Title
Publisher
IEEE
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.
Research Institute
Institute of Artificial Intelligence (IAI)