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Resource-Based Fitness Sharing

  • Jeffrey Horn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

This paper introduces a new algorithm for sharing to induce niching and speciation. Resource-based fitness sharing is a compromise between the very natural method of resource sharing and the practical technique of fitness sharing. Fitness sharing was meant to simulate resource sharing for function optimization problems, in which there are no explicit resources to share. Fitness sharing therefore cannot resolve resource-defined niches as can resource sharing. However, selection operators seem to have great difficulty handling the non-linear interactions among shared fitnesses under “natural resource sharing”. To obtain the benefits of both methods, we propose a sharing function that utilizes actual resources but in a form similar to that of fitness sharing, resulting in a set of linear equations for equilibrium, and hence much simpler dynamics under selection. The superiority of this compromise is demonstrated on a resource-coverage problem.

Keywords

Resource Sharing Learn Classifier System Corner Effect Initial Random Population Function Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Goldberg, D. E., Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimization. In: Grefenstette, J. (ed.): Proceedings of the 2nd International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, Hillsdale, New Jersey (1987) 1–8Google Scholar
  2. 2.
    Horn, J.: The Nature of Niching: Genetic Algorithms and the Evolution of Optimal, Cooperative Populations. Ph.D. thesis, University of Illinois at Urbana-Champaign, (UMI Dissertation Services, No. 9812622) (1997)Google Scholar
  3. 3.
    Mahfoud, S. W.: Niching Methods for Genetic Algorithms. Ph.D. thesis, University of Illinois at Urbana-Champaign (1995)Google Scholar
  4. 4.
    Horn, J., Goldberg, D. E., Deb. K.: Implicit Niching in a Learning Classifier System: Nature’s Way, Evolutionary Computation, 2(1) (1994) 37–66CrossRefGoogle Scholar
  5. 5.
    Dighe, R., Jakiela, M. J.: Solving Pattern Nesting Problems with Genetic Algorithms: Employing Task Decomposition and Contact Detection Between Adjacent Pieces. Evolutionary Computation 3(3) (1996) 239–266CrossRefGoogle Scholar
  6. 6.
    Kendall, G.: Applying Meta-Heuristic Algorithms to the Nesting Problem Utilising the No Fit Polygon. Ph.D. thesis, University of Nottingham (2000)Google Scholar
  7. 7.
    Horn J., Nafpliotis, N., Goldberg, D. E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. Proceedings of 1st IEEE International Conference on Evolutionary Computation, Volume 1. IEEE Service Center, Piscataway, New Jersey (1994) 82–87Google Scholar
  8. 8.
    Horn, J.: Multicriterion Decision Making. In: Bäck, T., Fogel, D. (ed.s): The Handbook of Evolutionary Computation. Oxford University Press, New York (1997) F1.9:1–15Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jeffrey Horn
    • 1
  1. 1.Department of Mathematics and Computer ScienceNorthern Michigan UniversityMarquette, MichiganUSA

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