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)


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.


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


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  1. 1.
    Coello Coello, C., Lamont, G.B., Veldhuizen, D.v.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007)zbMATHGoogle Scholar
  2. 2.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  3. 3.
    Graham, R.L., Lawer, E.L., Lenstra, J.K., Kan, A.H.G.R.: Optimization and Approximation in Deterministic Sequencing and Scheduling: A Survey. Annals of Discrete Mathematics 5, 287–326 (1979)zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Laumanns, M., Rudolph, G., Schwefel, H.P.: A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 241–249. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Süer, G.A., Báez, E., Czajkiewicz, Z.: Minimizing the number of tardy jobs in identical machine scheduling. Computers and Industrial Engineering 25(1–4), 243–246 (1993)CrossRefGoogle Scholar
  6. 6.
    Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)CrossRefGoogle Scholar

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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