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A Concurrent-Hybrid Evolutionary Algorithms with Multi-child Differential Evolution and Guotao Algorithm Based on Cultural Algorithm Framework

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Advances in Computation and Intelligence (ISICA 2010)

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Abstract

This paper proposes a multi-child differential evolutionary algorithm(MCDE), and forms a concurrent-hybrid evolutionary algorithm by integrating the MCDE algorithm and Guotao algorithm based on variable searching subspace(VSSGT) into the culture algorithm framework. Numerical experiment results indicate that the performance of the proposed algorithm is better than that of MCDE, Differential Evolution algorithm(DE) and VSSGT, and better than that of the DE with double trial vectors based on Boltzmann mechanism.

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References

  1. Liu, K.-q., Kang, L.-s., Zhao, Z.-z.: The Brief Report of Research on Cognizing the subarea of Evolutionary Computation (I). Computer Science 36(7) (2009)

    Google Scholar 

  2. Liu, K.-q., Kang, L.-s., Zhao, Z.-z.: The Brief Report of Research on Cognizing the subarea of Evolutionary Computation (II). Computer Science 36(8) (2009)

    Google Scholar 

  3. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. California Institute of Technology, Pasadena, California, USA, Tech. Rep. Caltech Concurrent Computation Program, Report 826 (1989)

    Google Scholar 

  4. John, J.: Grefenstette, Lamarckian learning in multi-agent environments. In: Proc. Fourth Intl. Conf. of Genetic Algorithms, pp. 303–310. Morgan Kaufmann, San Mateo (1991)

    Google Scholar 

  5. Krasnogor, N., Jim Smith, A.: Tutorial for Competent Memetic Algorithms:Model, Taxonomy and Design Issues. IEEE Transactions on Evolutionary Computation 10(6), 472–488 (2006)

    Google Scholar 

  6. Yong, L., Li-shan, K.: The Annealing evolution algorithm as function optimizer. Parallel Computing 21, 389–400 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  7. Kunqi, L.: Differential Evolution Algorithm Based on Simulated Annealing. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 120–126. Springer, Heidelberg (2007)

    Google Scholar 

  8. Wang, L., Jiao, L.: A novel genetic algorithim based on immunity. In: Proceedings of the 2000 IEEE International Symposium on Circuits and Systems(ISCAS), pp. 385–388 (2000)

    Google Scholar 

  9. Zhang, Q., Sun, J., Tsang, E.: Evolutionary Algorithm with Guided Mutation for the Maximum Clique Problem. IEEE Transaction on Evolutionary Computation 9(2), 192–200 (2005)

    Article  Google Scholar 

  10. Reynolds, R.G.: An Introduetion to Cultural Algorithms. In: Sebalk, Fogel, A.V., River Edge, J. (eds.) Proceedings of the 3th annual Conference on Evolution Programming, pp. 131–136. World Scientific Publishing, NJ (1994)

    Google Scholar 

  11. Jingbo, A., Hongfei, T.: Cultural based Particle Swarm Optimization Algorithm with Application. Liaoning, Dalian Uni. of Tech. (2005)

    Google Scholar 

  12. Storn, R., Price, K.: Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization (11), 341–359 (1997)

    Google Scholar 

  13. Tao, G., Kang, L.-s.: A new evolutionary algorithm for function optimization. Wuhan University Journal of Nature Sciences 4(4), 409–414 (1999)

    Article  MATH  Google Scholar 

  14. Kang, Z., Li, Y., Liu, P., Kang, L.-s.: An all-purpose evolutionary algorithm for solving nonlinear programming problems. Journal of computer research and development 39(11) (2002)

    Google Scholar 

  15. Wu, Z., Huang, H.: A differential evolution algorithm with double trial vectors based on Boltzmann mechanism. Journal of NanJing University (Natural Sciences) 44(2) (2008)

    Google Scholar 

  16. Vesterstrom, J., Thomsen, R.: A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems. In: Proceedings of the 2004 Congress on Evolutionary Computation, pp. 1980–1987 (2004)

    Google Scholar 

  17. Storn, R., Price, K.: Differential evolution – a simple and dfficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute, Berkley (1995)

    Google Scholar 

  18. Tao, G., Kang, L.-s.: A New Evolutionary Algorithm for Function Optimization. Journal of WuHan University (Natural Sciences) 4(4), 409–414 (1999)

    Article  MATH  Google Scholar 

  19. Guo, Y.-n., Wang, H.: Overview of cultural algorithms. Computer Engineering and Applications 45(9), 41–46 (2009)

    MathSciNet  Google Scholar 

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Li, X., Liu, K., Ma, L., Li, H. (2010). A Concurrent-Hybrid Evolutionary Algorithms with Multi-child Differential Evolution and Guotao Algorithm Based on Cultural Algorithm Framework. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_13

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  • DOI: https://doi.org/10.1007/978-3-642-16493-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16492-7

  • Online ISBN: 978-3-642-16493-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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