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Multi-Pareto-Ranking Evolutionary Algorithm

  • Wahabou Abdou
  • Christelle Bloch
  • Damien Charlet
  • François Spies
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7245)

Abstract

This paper proposes a new multi-objective genetic algorithm, called GAME, to solve constrained optimization problems. GAME uses an elitist archive, but it ranks the population in several Pareto fronts. Then, three types of fitness assignment methods are defined: the fitness of individuals depends on the front they belong to. The crowding distance is also used to preserve diversity. Selection is based on two steps: a Pareto front is first selected, before choosing an individual among the solutions it contains. The probability to choose a given front is computed using three parameters which are tuned using the design of experiments. The influence of the number of Pareto fronts is studied experimentally. Finally GAME’s performance is assessed and compared with three other algorithms according to the conditions of the CEC 2009 competition.

Keywords

Multiobjective optimization genetic algorithm Pareto ranking multiple fronts 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wahabou Abdou
    • 1
  • Christelle Bloch
    • 1
  • Damien Charlet
    • 1
  • François Spies
    • 1
  1. 1.FEMTO-ST InstituteUniversity of Franche-ComtéMontbéliardFrance

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