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An Effective Real-Parameter Genetic Algorithm for Multimodal Optimisation

  • P. J. Ballester
  • J. N. Carter

Abstract

Evolutionary Algorithms (EAs) are a useful tool to tackle real‐world optimisation problems. Two important features that make these problems hard are multimodality and high dimensionality of the search landscape.

In this paper, we present a real‐parameter Genetic Algorithm (GA) which is effective in optimising high dimensional, multimodal functions. We compare our algorithm with a previously published GA which the authors claim gives good results for high dimensional, multimodal functions. For problems with only few local optima, our algorithm does not perform as well as the other algorithm. However, for a problem with very many local optima, our algorithm performed significantly better.

Keywords

Unimodal Function Multimodal Function Multimodal Optimisation Rastrigin Function Multiple Optimal Solution 
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.
    Ballester, P. J. and Carter, J. N. (2003) Real-parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. Proceedings of the Genetic and Evolutionary Computation Conference, Ed. Erick Cantú-Paz et al. (Lecture Notes in Computer Science, Springer), 706–717.Google Scholar
  2. 2.
    Deb, K., Anand, A. and Joshi, D. (2002) A Computationally Efficient Evolutionary Algorithm for Real-parameter Optimization. Evolutionary Computation 10(4), 345–369.CrossRefGoogle Scholar
  3. 3.
    Deb, K. and Agrawal, S. (1995) Simulated Binary Crossover for Continuous Search Space. Complex Systems 9(2), 115–148.MathSciNetMATHGoogle Scholar
  4. 4.
    Deb, K. and Kumar, A. (1995) Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multi-modal and Multi-objective Problems. Complex Systems 9(6), 431–454.Google Scholar
  5. 5.
    Mengshoel, O. J. and Goldberg, D. E. (1999) Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement. Proceedings of the Genetic and Evolutionary Computation Conference, Ed. W. Banzhaf et al. (Morgan Kauff-mann), 409–416.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • P. J. Ballester
    • 1
  • J. N. Carter
    • 1
  1. 1.Department of Earth Science and EngineeringImperial College LondonLondonUK

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