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Proceedings of Fifth International Conference on Soft Computing for Problem Solving pp 343–350Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((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.

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References

  1. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan press, Ann Arbor (1975)

    Google Scholar 

  2. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)

    MATH  Google Scholar 

  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. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)

    MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  8. Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter evolution. Evol. Comput. J. 10(4), 371–395 (2002)

    Article  Google Scholar 

  9. Deep, K., Thakur, M.: A new crossover operator for real coded genetic algorithms. Appl. Math. Comput. 188, 895–911 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  11. Kaelo, P., Ali, M.M.: Integrated crossover rules in real coded genetic algorithms. Eur. J. Oper. Res. 176, 60–76 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. Thakur, M.: A new genetic algorithm for global optimization of multimodal continuous functions. J. Comput. Sci. 5, 298–311 (2014)

    Article  MathSciNet  Google Scholar 

  13. Deep, K., Thakur, M.: A new mutation operator for real coded genetic algorithms. Appl. Math. Comput. 193, 229–247 (2007)

    MathSciNet  MATH  Google Scholar 

  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 

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Acknowledgments

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

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Authors and Affiliations

  1. Control and Decision Systems Laboratory, Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India

    Shashi

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

Authors
  1. Shashi
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  2. Kusum Deep
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Corresponding author

Correspondence to Shashi .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Dept of Mathematics, South Asian University New Delhi, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Shashi, Deep, K. (2016). A Novel Crossover Operator Designed to Exploit Synergies of Two Crossover Operators for Real-Coded Genetic Algorithms. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_32

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

  • eBook Packages: EngineeringEngineering (R0)

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