An improved primal-dual genetic algorithm for optimization in dynamic environments

Authors:
Hongfeng Wang;Dingwei Wang
Affiliations:
Institute of Systems Engineering, Northeastern University, P.R. China;Institute of Systems Engineering, Northeastern University, P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

Inspired by the complementary and dominance mechanism in nature, the Primal-Dual Genetic Algorithm (PDGA) has been proved successful in dynamic environments. In this paper, an important operator in PDGA, primal-dual mapping, is discussed and a new statistics-based primal-dual mapping scheme is proposed. The experimental results on the dynamic optimization problems generated from a set of stationary benchmark problems show that the improved PDGA has stronger adaptability and robustness than the original for dynamic optimization problems.