Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5815
Title: Adaptive primal-dual genetic algorithms in dynamic environments
Authors: Wang, H
Yang, S
Ip, WH
Wang, D
Keywords: Adaptive dominant replacement scheme;Lamarckian learning;Dynamic optimization problem (DOP);Genetic algorithm (GA)
Issue Date: 2009
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39(6): 1348 - 1361, Dec 2009
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: This article is placed here with permission of IEEE - Copyright @ 2010 IEEE
URI: http://bura.brunel.ac.uk/handle/2438/5815
DOI: http://dx.doi.org/10.1109/TSMCB.2009.2015281
ISSN: 1083-4419
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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