Journal of Computer Science and Technology

, Volume 23, Issue 1, pp 64–76 | Cite as

Spatially-Structured Sharing Technique for Multimodal Problems

  • Grant DickEmail author
  • Peter Whigham
Regular Paper


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).


evolutionary algorithm multimodal problem domain sharing spatially-structured population 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Mahfoud S W. Niching methods for genetic algorithms [Dissertation]. University of Illinois at Urbana-Champaign, Urbana, IL, USA, IlliGAL Report 95001, May 1995.Google Scholar
  2. [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.Google Scholar
  3. [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. [4]
    Tomassini M. Spatially Structured Evolutionary Algorithms. Springer, 2005.Google Scholar
  5. [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. [6]
    Holland J H. Adaptation in Natural and Artificial Systems. Cambridge, Massachusetts: The MIT Press, 2ed., 1992.Google Scholar
  7. [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. [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.CrossRefGoogle Scholar
  9. [9]
    Sareni B, Krähenbuhl L. Fitness sharing and niching methods revisited. IEEE Trans. Evolutionary Computation, 1998, 2(3): 97–106.CrossRefGoogle Scholar
  10. [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.CrossRefGoogle Scholar
  11. [11]
    Mahfoud S W. Niching methods for genetic algorithms [Dissertation]. University of Illinois at Urbana-Champaign, Urbana, IL, USA, IlliGAL Report 95001, May 1995.Google Scholar
  12. [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.Google Scholar
  13. [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. [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.Google Scholar
  15. [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.Google Scholar
  16. [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.CrossRefGoogle Scholar
  17. [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.CrossRefGoogle Scholar
  18. [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.CrossRefGoogle Scholar
  19. [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. [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. [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. [22]
    Wright S. Isolation by distance. Genetics, 1943, 28(2): 114–138.Google Scholar
  23. [23]
    Mayr E. Populations, Species and Evolution; An Abridgment of Animal Species and Evolution. Harvard University Press, 1970.Google Scholar
  24. [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.Google Scholar
  25. [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. [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. [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. [28]
    Sarma J. An analysis of decentralized and spatially distributed genetic algorithms [Dissertation]. George Mason University, Fairfax VA, USA, 1998.Google Scholar
  29. [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. [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. [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.Google Scholar
  32. [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.zbMATHGoogle Scholar

Copyright information

© Science Press, Beijing, China and Springer Science + Business Media, LLC, USA 2008

Authors and Affiliations

  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

Personalised recommendations