An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments

  • Hongfeng Wang
  • Dingwei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


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.


Genetic Algorithm Dynamic Environment Fitness Landscape Distance Space Dynamic Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hongfeng Wang
    • 1
  • Dingwei Wang
    • 1
  1. 1.Institute of Systems EngineeringNortheastern UniversityP.R. China

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