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Improving the Performance and Scalability of Differential Evolution

  • Antony W. Iorio
  • Xiaodong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to Differential Evolution that introduces greater diversity. The new DE approach demonstrates fast convergence towards the global optimum and is highly scalable in the decision space.

Keywords

Differential Evolution Optimization Rotational Invariance 

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References

  1. 1.
    Salomon, R.: Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions: A survey of some theoretical and practical aspects of genetic algorithms. Bio. Systems 39(3), 263–278 (1996)CrossRefGoogle Scholar
  2. 2.
    Price, K.: Differential evolution: a fast and simple numerical optimizer. In: Biennial Conference of the North American Fuzzy Information Processing Society, New York, vol. 3339, pp. 524–527 (1996)Google Scholar
  3. 3.
    Sutton, A.M., Lunacek, M., Whitley, L.D.: Differential evolution and non-separability: using selective pressure to focus search. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1428–1435 (2007)Google Scholar
  4. 4.
    Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, pp. 76–83 (2000)Google Scholar
  5. 5.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Deb, K., Goyal, M.: A combined genetic adaptive search (GENEAS) for engineering design. Computer Science and Informatics 26(4), 30–45 (1995)Google Scholar
  7. 7.
    Iorio, A., Li, X.: Rotated test problems for assessing the performance of multi-objective optimization algorithms. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 683–690 (2006)Google Scholar
  8. 8.
    Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–304. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Antony W. Iorio
    • 1
  • Xiaodong Li
    • 1
  1. 1.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia

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