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Spatially-Structured Sharing Technique for Multimodal Problems

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Abstract

Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm (EA). However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. Niching methods, such as fitness sharing, allow an EA to maintain multiple solutions in a single population, however they have rarely been used in conjunction with spatially-structured populations. This paper introduces local sharing, a method that applies sharing to the overlapping demes of a spatially-structured population. The combination of these two methods succeeds in maintaining multiple solutions in problems that have previously proved difficult for sharing alone (and vice-versa).

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References

  1. Mahfoud S W. Niching methods for genetic algorithms [Dissertation]. University of Illinois at Urbana-Champaign, Urbana, IL, USA, IlliGAL Report 95001, May 1995.

  2. Goldberg D E, Richardson J. Genetic algorithms with sharing for multi-modal function optimisation. In Proc. the 2nd Int. Conf. Genetic Algorithms and Their Applications, Massachusetts, USA, 1987, pp.41–49.

  3. Goldberg D E, Deb K, Horn J. Massive Multimodality, Deception, and Genetic Algorithms. Parallel Problem Solving from Nature, 2, Männer R, Manderick B (eds.), Amsterdam: Elsevier Science Publishers, B. V., 1992, pp.37–46.

    Google Scholar 

  4. Tomassini M. Spatially Structured Evolutionary Algorithms. Springer, 2005.

  5. Spears W M. Simple subpopulation schemes. In Proc. the Third Annual Conf. Evolutionary Programming, Sebald A V, Fogel L J (eds.), Singapore, World Scientific Press, 1994, pp.296–307.

    Google Scholar 

  6. Holland J H. Adaptation in Natural and Artificial Systems. Cambridge, Massachusetts: The MIT Press, 2ed., 1992.

    Google Scholar 

  7. Deb K, Goldberg D E. An investigation of niche and species formation in genetic function optimization, In Proc. the Third Int. Conf. Genetic Algorithms, Schaffer J D (ed.), San Mateo, CA, Morgan Kaufmann, 1989, pp.42–50.

    Google Scholar 

  8. Darwen P, Yao Y. Every niching method has its niche: Fitness sharing and implicit sharing compared. In Proc. Parallel Problem Solving from Nature — PPSN IV,Voigt H M, Ebeling W, Rechenberg I, Schwefel H P (eds.), Berlin, Springer, 1996, pp.398–407.

    Chapter  Google Scholar 

  9. Sareni B, Krähenbuhl L. Fitness sharing and niching methods revisited. IEEE Trans. Evolutionary Computation, 1998, 2(3): 97–106.

    Article  Google Scholar 

  10. Cioppa A D, Stefano C D, Marcelli A. On the role of population size and niche radius in fitness sharing. IEEE Trans. Evolutionary Computation, 2004, 8(6): 580–592.

    Article  Google Scholar 

  11. Mahfoud S W. Niching methods for genetic algorithms [Dissertation]. University of Illinois at Urbana-Champaign, Urbana, IL, USA, IlliGAL Report 95001, May 1995.

  12. Horn J. The nature of niching: Genetic algorithms and the evolution of optimal, cooperative populations [Dissertation]. University of Illinois at Urbana Champaign, Urbana, Illinois, 1997.

  13. Watson J P. A performance assessment of modern niching methods for parameter optimization problems. In Proc. the Genetic and Evolutionary Computation Conference, Banzhaf W, Daida J, Eiben A E et al. (eds.), Orlando, Florida, USA, vol. 1, Morgan Kaufmann, July 13–17, 1999, pp.702–709.

    Google Scholar 

  14. Miller B L, Shaw M J. Genetic algorithms with dynamic niche sharing for multimodal function optimization. In Proc. International Conference on Evolutionary Computation, Nayoya University, Japan, 1996, pp.786–791.

  15. Pétrowski A. A clearing procedure as a niching method for genetic algorithms. In Proc. the 1996 IEEE Int. Conf. Evolutionary Computation, Nayoya University, Japan, 1996, pp.798–803.

  16. Li Jian-Ping, Balazs M E, Parks G T, Clarkson P J. A species conserving genetic algorithm for multimodal function optimization. Evolutionary Computation, 2002, 10(3): 207–234.

    Article  Google Scholar 

  17. Parrott D, Li Xiao-Dong. Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evolutionary Computation, 2006, 10(4): 440–458.

    Article  Google Scholar 

  18. Bird S, Li Xiaodong. Adaptively choosing niching parameters in a PSO. In Proc. the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, Keijzer M, Cattolico M, Arnold D et al. (eds.), Seattle, Washington, USA, vol 1, ACM Press, July 8–12, 2006, pp.3–10.

    Chapter  Google Scholar 

  19. Bird S, Li Xiaodong. Enhancing the robustness of a speciation-based PSO. In Proc. the 2006 IEEE Congress on Evolutionary Computation, Yen G G, Lucas S M, Fogel G et al. (eds.), Vancouver, BC, Canada, IEEE Press, July 16–21, 2006, pp.843–850.

    Google Scholar 

  20. Smith R E, Forrest S, Perelson A S. Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation, 1993, 1(2): 127–149.

    Google Scholar 

  21. Forrest S, Smith R E, Javornik B, Perelson A S. Using genetic algorithms to explore pattern recognition in the immune system. Evolutionary Computation, 1993, 1(3): 191–211.

    Google Scholar 

  22. Wright S. Isolation by distance. Genetics, 1943, 28(2): 114–138.

    Google Scholar 

  23. Mayr E. Populations, Species and Evolution; An Abridgment of Animal Species and Evolution. Harvard University Press, 1970.

  24. De Jong K A. An analysis of the behavior of a class of genetic adaptive systems [Dissertation]. University of Michigan, Ann Arbor, MI, 1975, Dissertation Abstracts International, University Microfilms Number 76-9381, 36(10): 5140B.

  25. Mahfoud S W. A comparison of parallel and sequential niching methods. In Proc. the Sixth International Conference on Genetic Algorithms, Eshelman L (ed.), San Francisco, CA, Morgan Kaufmann, 1995, pp.136–143.

    Google Scholar 

  26. Mahfoud S W. Population size and genetic drift in fitness sharing. In Proc. Foundations of Genetic Algorithms 3, Whitley L D, Vose M D (eds.), San Francisco, Morgan Kaufmann, 1995, pp.185–224.

    Google Scholar 

  27. Baker J E. Reducing bias and inefficiency in the selection algorithm. In Proc. Genetic Algorithms and Their Applications (ICGA'87), Grefenstette J J (ed.), Hillsdale, New Jersey, Lawrence Erlbaum Associates, 1987, pp.14–21.

    Google Scholar 

  28. Sarma J. An analysis of decentralized and spatially distributed genetic algorithms [Dissertation]. George Mason University, Fairfax VA, USA, 1998.

  29. Horn J, Goldberg D E. Genetic algorithm difficulty and the modality of fitness landscapes. In Proc. Foundations of Genetic Algorithms 3, Whitley L D, Vose M D (eds.), Morgan Kaufmann, San Francisco, CA, 1995, pp.243–269.

    Google Scholar 

  30. Beasley D, Bull D R, Martin R R. A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1993, 1(2): 101–125.

    Google Scholar 

  31. Dick G. A comparison of localised and global niching methods. In Proc. The 17th Annual Colloquium of the Spatial Information Research Centre, Dunedin, New Zealand, 2005, pp.91–101.

  32. Goldberg D E. A note on Boltzmann tournament selection for genetic algorithms and population–oriented simulated annealing. Complex Systems, 1990, 4(4): 445–460.

    MATH  Google Scholar 

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  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Grant Dick & Peter Whigham

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  1. Grant Dick
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  2. Peter Whigham
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Correspondence to Grant Dick.

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Dick, G., Whigham, P. Spatially-Structured Sharing Technique for Multimodal Problems. J. Comput. Sci. Technol. 23, 64–76 (2008). https://doi.org/10.1007/s11390-008-9110-6

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  • DOI: https://doi.org/10.1007/s11390-008-9110-6

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