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Incorporating Distance Domination in Multiobjective Evolutionary Algorithm

  • Praveen K. Tripathi
  • Sanghamitra Bandyopadhyay
  • Sankar K. Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

In this article we propose a novel distance domination parameter and describe a multiobjective evolutionary concept called distance domination based multiobjective evolutionary algorithm (DBMEA). The distance parameter drives the algorithm faster in approximating the Pareto optimal front. To ensure proper diversity in the solutions of the non-dominating set, a new method for incorporating diversity is explained. The DBMEA has been compared with the NSGA-II algorithm on different test functions using different performance measures.

Keywords

Test Problem Multiobjective Evolutionary Algorithm Convergence Measure Vector Evaluate Genetic Algorithm Standard Test Function 
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 2005

Authors and Affiliations

  • Praveen K. Tripathi
    • 1
  • Sanghamitra Bandyopadhyay
    • 1
  • Sankar K. Pal
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkata

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