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A Novel Crossover Operator Designed to Exploit Synergies of Two Crossover Operators for Real-Coded Genetic Algorithms

  • Shashi
  • Kusum Deep
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 437)

Abstract

In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. The performance of DDX is compared with existing real-coded crossover operator namely Laplace crossover (LX). DDX is used in conjunction with a well-known mutation operator; Power mutation (PM) to obtain a new generational real-coded genetic algorithm called DDX-PM. DDX-PM is compared with the existing LX-PM. The performance of both the genetic algorithms is compared on the basis of success rate, average function evaluation, average error and computational time, and the preeminence of the proposed crossover operator is established.

Keywords

Real-coded genetic algorithm Mutation operator Crossover operator 

Notes

Acknowledgments

Shashi thankfully acknowledge the financial assistance from National Board of Higher Mathematics, Department of Atomic Energy, Government of India.

References

  1. 1.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan press, Ann Arbor (1975)Google Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)zbMATHGoogle Scholar
  3. 3.
    Spears, W.M.: Crossover or mutation? In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2, pp. 221–238. Morgan Kaufmann, San Mateo, CA (1993)Google Scholar
  4. 4.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Hong, I., Kahng, A.B., Moon, B.R.: Exploiting synergies of multiple crossovers: initial studies. In: Proceedings of Second IEEE International Conference on Evolutionary Computation, IEEE Press, Piscataway, NJ, pp. 245–250 (1995)Google Scholar
  6. 6.
    Yoon, H.S., Moon, B.R.: An empirical study on the synergy of multiple crossover operators. IEEE Trans. Evol. Comput. 6(2), 212–223 (2002)CrossRefGoogle Scholar
  7. 7.
    Herrera, F., Lozano, M., Sanchez, A.M.: Hybrid crossover operators for real coded genetic algorithms: an experimental study. Soft. Comput. 9(4), 280–298 (2005)CrossRefzbMATHGoogle Scholar
  8. 8.
    Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter evolution. Evol. Comput. J. 10(4), 371–395 (2002)CrossRefGoogle Scholar
  9. 9.
    Deep, K., Thakur, M.: A new crossover operator for real coded genetic algorithms. Appl. Math. Comput. 188, 895–911 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Tutkun, N.: Optimization of multimodal continuous functions using a new crossover for the real-coded genetic algorithms. Expert Syst. Appl. 36, 8172–8177 (2009)CrossRefGoogle Scholar
  11. 11.
    Kaelo, P., Ali, M.M.: Integrated crossover rules in real coded genetic algorithms. Eur. J. Oper. Res. 176, 60–76 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Thakur, M.: A new genetic algorithm for global optimization of multimodal continuous functions. J. Comput. Sci. 5, 298–311 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Deep, K., Thakur, M.: A new mutation operator for real coded genetic algorithms. Appl. Math. Comput. 193, 229–247 (2007)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Deep, K., Shashi., Katiyar, V.K.: Global optimization of Lennard-Jones potential using newly developed real coded genetic algorithms. In: Proceedings of 2011—International Conference on Communication Systems and Network Technologies, (IEEE Computer Society Proceedings), pp. 614–618 (2011)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Control and Decision Systems Laboratory, Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia
  2. 2.Department of MathematicsIndian Institute of Technology RoorkeeRoorkeeIndia

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