Adaptive Primal-Dual Genetic Algorithms in Dynamic Environments. Wang, H., Yang, S., Ip, W., & Wang, D. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(6):1348--1361, 2009. ![Adaptive Primal-Dual Genetic Algorithms in Dynamic Environments [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{wang_adaptive_2009,
title = {Adaptive {Primal}-{Dual} {Genetic} {Algorithms} in {Dynamic} {Environments}},
volume = {39},
issn = {1083-4419},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4838965},
doi = {10.1109/TSMCB.2009.2015281},
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.},
number = {6},
journal = {Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on},
author = {Wang, Hongfeng and Yang, Shengxiang and Ip, W.H. and Wang, Dingwei},
year = {2009},
keywords = {Genetic algorithms, PDGA, adaptive Lamarckian learning mechanism, adaptive dominant replacement scheme, complementary mechanism, dominance mechanism, dynamic optimization problem, dynamic programming, inferior chromosome string, learning (artificial intelligence), mathematical operators, primal-dual genetic algorithm, primal-dual mapping scheme, probability-based PDM operator, statistical distribution, statistical distributionsDOP},
pages = {1348--1361}
}
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