Mutagenesis as a Diversity Enhancer and Preserver in Evolution Strategies

  • José L. Guerrero
  • Alfonso Gómez-Jordana
  • Antonio Berlanga
  • José M. Molina
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)

Abstract

Mutagenesis is a process which forces the coverage of certain zones of the search space during the generations of an evolution strategy, by keeping track of the covered ranges for the different variables in the so called gene matrix. Originally introduced as an artifact to control the automated stopping criterion in a memetic algorithm, ESLAT, it also improved the exploration capabilities of the algorithm, even though this was considered a secondary matter and not properly analyzed or tested. This work focuses on this diversity enhancement, redefining mutagenesis to increase this characteristic, measuring this improvement over a set of twenty-seven unconstrained optimization functions to provide statistically significant results.

Keywords

Search Space Evolution Strategy Gene Matrix Evolutionary Computation Memetic Algorithm 
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.
    Arioli, M., Duff, I., Ruiz, D.: Stopping criteria for iterative solvers. SIAM Journal on Matrix Analysis and Applications 13, 138 (1992)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Back, T., Fogel, D., Michalewicz, Z.: Handbook of evolutionary computation. IOP Publishing Ltd. (1997)Google Scholar
  3. 3.
    Broyden, C.: The convergence of a class of double-rank minimization algorithms 1. general considerations. IMA Journal of Applied Mathematics 6(1), 76 (1970)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Cantu-Paz, E.: Efficient and accurate parallel genetic algorithms. Springer, Netherlands (2000)MATHCrossRefGoogle Scholar
  5. 5.
    Coello, C., Lamont, G., Van Veldhuizen, D.: Evolutionary algorithms for solving multi-objective problems. Springer-Verlag New York Inc. (2007)Google Scholar
  6. 6.
    Eshelman, L., Schaffer, J.: Preventing premature convergence in genetic algorithms by preventing incest. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 115–122. Morgan Kaufmann Publishers (1991)Google Scholar
  7. 7.
    García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec2005 special session on real parameter optimization. Journal of Heuristics 15(6), 617–644 (2009)MATHCrossRefGoogle Scholar
  8. 8.
    Guerrero, J., Berlanga, A., Molina, J.: A robust memetic algorithm with self-stopping capabilities. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 113–114. ACM (2011)Google Scholar
  9. 9.
    Hansen, N., Müller, S., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation 11(1), 1–18 (2003)CrossRefGoogle Scholar
  10. 10.
    Kelley, C.: Detection and remediation of stagnation in the Nelder-Mead algorithm using a sufficient decrease condition. SIAM Journal on Optimization 10(1), 43–55 (2000)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Transactions on Evolutionary Computation 9(5), 474–488 (2005)CrossRefGoogle Scholar
  12. 12.
    Michalewicz, Z., Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, vol. 19. Springer, Berlin (1992)MATHGoogle Scholar
  13. 13.
    Talbi, E.: Metaheuristics: From design to implementation. Wiley Online Library (2009)Google Scholar
  14. 14.
    Toffolo, A., Benini, E.: Genetic diversity as an objective in multi-objective evolutionary algorithms. Evolutionary Computation 11(2), 151–167 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • José L. Guerrero
    • 1
  • Alfonso Gómez-Jordana
    • 1
  • Antonio Berlanga
    • 1
  • José M. Molina
    • 1
  1. 1.Computer Science Department, Group of Applied Artificial Intelligence.University Carlos III of MadridMadridSpain

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