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An Approach to Instantly Use Single-Objective Results for Multi-objective Evolutionary Combinatorial Optimization

  • Christian Grimme
  • Joachim Lepping
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7219)

Abstract

Standard dominance-based multi-objective evolutionary algorithms hardly allow to integrate problem knowledge without redesigning the approach as a whole. We present a flexible alternative approach based on an abstraction from predator-prey interplay. For parallel machine scheduling problems, we find that the combination of problem knowledge principally leads to better trade-off approximations compared to standard class of algorithms, especially NSGA-2. Further, we show that the incremental integration of existing problem knowledge gradually improves the algorithm’s performance.

Keywords

Predator-Prey Model Evolutionary Multi-Objective Optimization Multi-objective Scheduling Knowledge Integration 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Grimme
    • 1
  • Joachim Lepping
    • 2
  1. 1.Robotics Research InstituteTU Dortmund UniversityDortmundGermany
  2. 2.INRIA Rhône-AlpesGrenoble UniversityMontbonnot-Saint-MartinFrance

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