Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12725
Title: Adaptive primal-dual genetic algorithms in dynamic environments
Authors: Wang, H
Yang, S
Ip, WH 
Wang, D
Keywords: Adaptive dominant replacement scheme
Algorithm design and analysis
Dynamic optimization problem (DOP)
Dynamic programming
Genetic algorithm (GA)
Genetic algorithms
Lamarckian learning
Optimization methods
Primal-dual mapping (PDM)
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2009, v. 39, no. 6, p. 1348-1361 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
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.
URI: http://hdl.handle.net/10397/12725
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2009.2015281
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