Evolutionary Multi-Criterion Optimization

Volume 2632 of the series Lecture Notes in Computer Science pp 391-404


Searching under Multi-evolutionary Pressures

  • Hussein A. AbbassAffiliated withArtifficial Life and Adaptive Robotics (A.L.A.R.) Lab, School of Computer Science, University of New South Wales, Australian Defence Force Academy Campus
  • , Kalyanmoy DebAffiliated withMechanical Engineering Department, Indian Institute of Technology, Kanpur

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A number of authors made the claim that a multiobjective approach preserves genetic diversity better than a single objective approach. Sofar, none of these claims presented a thorough analysis to the effect of multiobjective approaches. In this paper, we provide such analysis and show that a multiobjective approach does preserve reproductive diversity. We make our case by comparing a pareto multiobjective approach against a single objective approach for solving single objective global optimization problems in the absence of mutation. We show that the fitness landscape is different in both cases and the multiobjective approach scales faster and produces better solutions than the single objective approach.