Improving Evolutionary Multi-objective Optimization Using Genders

  • Zdzislaw Kowalczuk
  • Tomasz Bialaszewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


In solving highly dimensional multi-objective optimization (EMO) problems by evolutionary computations the concept of Pareto-domination appears to be not effective. The paper discusses a new approach to EMO by introducing a concept of genetic genders for the purpose of making distinction between different groups of objectives. This approach is also able to keep diversity among the Pareto-optimal solutions produced.


Genetic Algorithm Pareto Front Multiobjective Optimization Evolutionary Multiobjective Optimization Parental Pool 
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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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zdzislaw Kowalczuk
    • 1
  • Tomasz Bialaszewski
    • 1
  1. 1.Faculty of Electronics, Telecommunication and Computer ScienceGdansk University of TechnologyGdanskPoland

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