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Parent to Mean-Centric Self-Adaptation in SBX Operator for Real-Parameter Optimization

  • Himanshu Jain
  • Kalyanmoy Deb
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

Abstract

Most real-parameter genetic algorithms (RGAs) use a blending of participating parent solutions to create offspring solutions in its recombination operator. The blending operation creates solutions either around one of the parent solutions (having a parent-centric approach) or around the centroid of the parent solutions (having a mean-centric approach). In this paper, we argue that a self-adaptive approach in which a parent or a mean-centric approach is adopted based on population statistics is a better procedure than either approach alone. We propose a self-adaptive simulated binary crossover (SA-SBX) approach for this purpose. On a test suite of six unimodal and multi-modal test problems, we demonstrate that a RGA with SA-SBX approach performs consistently better in locating the global optimum solution than RGA with original SBX operator and RGA with mean-centric SBX operator.

Keywords

Self Adaptation Real coded Genetic Algorithms Crossover 

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References

  1. 1.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9(2), 115–148 (1995)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation Journal 10(4), 371–395 (2002)CrossRefGoogle Scholar
  3. 3.
    Deb, K., Sindhya, K., Okabe, T.: Self-adaptive simulated binary crossover for real-parameter optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 1187–1194. The Association of Computing Machinery (ACM), New York (2007)Google Scholar
  4. 4.
    Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms 2 (FOGA-2), pp. 187–202 (1993)Google Scholar
  5. 5.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation Journal 9(2), 159–195 (2000)CrossRefGoogle Scholar
  6. 6.
    Higuchi, T., Tsutsui, S., Yamamura, M.: Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 365–374. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Ono, I., Kobayashi, S.: A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover. In: Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA-7), pp. 246–253 (1997)Google Scholar
  8. 8.
    Voigt, H.-M., Mühlenbein, H., Cvetković, D.: Fuzzy recombination for the Breeder Genetic Algorithm. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 104–111 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Himanshu Jain
    • 1
  • Kalyanmoy Deb
    • 1
  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia

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