UEGO, an abstract niching technique for global optimization

  • Márk Jelasity
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

In this paper, uego, a new general technique for accelerating and/or parallelizing existing search methods is suggested. uego is a generalization and simplification of gas, a genetic algorithm (ga) with subpopulation support. With these changes, the niching technique of gas can be applied along with any kind of optimizers. Besides this, uego can be effectively parallelized. Empirical results are also presented which include an analysis of the effects of the user-given parameters and a comparison with a hill climber and a ga.

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References

  1. 1.
    Thomas Bäck, editor. Proceedings of the Seventh International Conference on Genetic Algorithms, San Francisco, California, 1997. Morgan Kaufmann.Google Scholar
  2. 2.
    D. Beasley, D. R. Bull, and R. R. Martin. A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1(2):101–125, 1993.Google Scholar
  3. 3.
    K. Deb. Genetic algorithms in multimodal function optimization. TCGA report no. 89002, The University of Alabama, Dept. of Engineering mechanics, 1989.Google Scholar
  4. 4.
    K. Deb and David E. Goldberg. An investegation of niche and species formation in genetic function optimization. In J. D. Schaffer, editor, The Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, 1989.Google Scholar
  5. 5.
    A. E. Eiben and J. K. van der Hauw. Graph coloring with adaptive genetic algorithms. Journal of Heuristics, 4(1), 1998.Google Scholar
  6. 6.
    J. J. Grefenstette. Genesis: A system for using genetic search procedures. In Proceedings of the 1984 Conference on Intelligent Systems and Machines, pages 161–165, 1984.Google Scholar
  7. 7.
    Hisao Ishibuchi, Tadahiko Murata, and Shigemitsu Tomioka. Effectiveness of genetic local search algorithms. In Bäck [1], pages 505–512.Google Scholar
  8. 8.
    Márk Jelasity. A wave analysis of the subset sum problem. In Bäck [1], pages 89–96.Google Scholar
  9. 9.
    Márk Jelasity and József Dombi. GAS, a concept on modeling species in genetic algorithms. Artificial Intelligence, 99(1):1–19, 1998.MATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    A. Juels and M. Wattenberg. Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Technical report, UC Berkeley, 1994.Google Scholar
  11. 11.
    S. Khuri, T. Bäck, and J. Heitkötter. An evolutionary approach to combinatorial optimization problems. In The Proceedings of CSC'94, 1993.Google Scholar
  12. 12.
    M. Mitchell, J. H. Holland, and S. Forrest. When will a genetic algorithm outper-form hillclimbing? In J. D. Cowan et al., editors, Advances in Neural Information Processing Systems 6. Morgan Kaufmann, 1994.Google Scholar
  13. 13.
    Mutsunori Yagiura and Toshihide Ibaraki. Genetic and local search algorithms as robust and simple optimization tools. In Ibrahim H. Osman and James P. Kelly, editors, Meta-Heuristics: Theory and Application, pages 63–82. Kluwer Academic Publishers, 1996.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Márk Jelasity
    • 1
  1. 1.Research Group on Artificial IntelligenceMTA-JATESzegedHungary

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