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Using supportive coevolution to evolve self-configuring crossover

Published:06 July 2013Publication History

ABSTRACT

Creating an Evolutionary Algorithm (EA) which is capable of automatically configuring itself and dynamically controlling its parameters is a challenging problem. However, solving this problem can reduce the amount of manual configuration required to implement an EA, allow the EA to be more adaptable, and produce better results on a range of problems without requiring problem specific tuning. Using Supportive Coevolution (SuCo) to evolve Self-Configuring Crossover (SCX) combines the automatic configuration technique of multiple populations from SuCo with the dynamic crossover operator creation and evolution of SCX.

This paper reports an empirical comparison and analysis of several different combinations of mutation and crossover techniques including SuCo and SCX. The Rosenbrock, Rastrigin, and Offset Rastrigin benchmark problems were selected for testing purposes. The benefits and drawbacks of self-adaptation and evolution of SCX are also discussed. SuCo of mutation step sizes and SCX operators produced results that were at least as good as previous work, and some experiments produced results that were significantly better.

References

  1. L. Dioşan and M. Oltean. Evolving crossover operators for function optimization. In Proceedings of the 9th European Conference on Genetic Programming, volume 3905 of Lecture Notes in Computer Science, pages 97--108, Budapest, Hungary, 10 - 12 Apr. 2006. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B. W. Goldman and D. R. Tauritz. Meta-Evolved Empirical Evidence of the Effectiveness of Dynamic Parameters. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '11), pages 155--156, Dublin, Ireland, July 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. B. W. Goldman and D. R. Tauritz. Self-Configuring Crossover. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '11), pages 575--582, Dublin, Ireland, July 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. B. W. Goldman and D. R. Tauritz. Supportive Coevolution. In Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '12), pages 59--66, Philadelphia, Pennsylvania, USA, July 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Gomez. Self Adaptation of Operator Rates in Evolutionary Algorithms. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC '04), pages 1720--1726, Portland, Oregon, USA, 20-23 June 2004. IEEE Press.Google ScholarGoogle ScholarCross RefCross Ref
  6. D. S. H. Mühlenbein and J. Born. The Parallel Genetic Algorithm as Function Optimizer. Parallel Computing, 17(6-7):619--632, Sept. 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. K. A. D. Jong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan, 1975. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. G. Papa. Parameter-less Evolutionary Search. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO '08), pages 1133--1134, Atlanta, Georgia, USA, 12-16 July 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. E. A. Smorodkina and D. R. Tauritz. Toward Automating EA Configuration: the Parent Selection State. In Proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC'07), pages 63--70, Singapore, Sept. 2007.Google ScholarGoogle ScholarCross RefCross Ref
  10. F. Vafaee, W. Xiao, P. C. Nelson, and C. Zhou. Adaptively evolving probabilities of genetic operators. In Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA '08), pages 292--299, La Jolla, San Diego, USA, 11-13 Dec. 2008. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. E. W. de Landgraaf and V. Nannen. Parameter Calibration Using Meta-Algorithms. In Proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC'07), pages 71--78, Singapore, Sept. 2007.Google ScholarGoogle Scholar
  12. J. R. Woodward and J. Swan. The Automatic Generation of Mutation Operators for Genetic Algorithms. In Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '12), pages 67--74, Philadelphia, Pennsylvania, USA, July 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

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              • Published in

                cover image ACM Conferences
                GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
                July 2013
                1798 pages
                ISBN:9781450319645
                DOI:10.1145/2464576
                • Editor:
                • Christian Blum,
                • General Chair:
                • Enrique Alba

                Copyright © 2013 ACM

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                Publication History

                • Published: 6 July 2013

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