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Differential evolution strategies with random forest regression in the bat algorithm

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Published:06 July 2013Publication History

ABSTRACT

In this paper, we present a novel solution for the hybridization of the bat algorithm with differential evolution strategies and a random forests machine learning method. Extensive experiments and tests on standard benchmark functions have shown that these hybridized algorithms improved the original bat algorithm significantly.

References

  1. C. Blum and X. Li. Swarm intelligence in optimization. In C. Blum and D. Merkle, editors, Swarm Intelligence: Introduction and Applications, pages 43--86. Springer Verlag, Berlin, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  2. L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Das and P. Suganthan. Differential evolution: A survey of the state-of-the-art. Evolutionary Computation, IEEE Transactions on, 15(1):4--31, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Eiben and J. Smith3 Introduction to Evolutionary Computing. Springer-Verlag, Berlin, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. I. Fister and J. Using differential evolution for the graph coloring. pages 150--156, 2011.Google ScholarGoogle Scholar
  6. I. Fister, D. Fister, and X.-S. Yang. A hybrid bat algorithm. Electrotechnical review, 2013, In press.Google ScholarGoogle Scholar
  7. I. Fister, I. Fister, J. Brest, and V. Žumer. Memetic artificial bee colony algorithm for large-scale global optimization. In IEEE Congress on Evolutionary Computation, pages 1--8, 2012.Google ScholarGoogle Scholar
  8. I. Fister, X.-S. Yang, I. Fister, and J. Brest. Memetic firefly algorithm for combinatorial optimization. In B. Filipič and J. Šilc, editors, Bioinspired optimization methods and their applications: proceedings of the Fifth International Conference on Bioinspired Optimization Methods and their Applications - BIOMA 2012, pages 75--86. Jožef Stefan Institute, 2012.Google ScholarGoogle Scholar
  9. S. Kirkpatrick, C. Gelatt, and M. Vecchi. Optimization by simulated annealing. Science, 220(4598):671--680, 1983.Google ScholarGoogle ScholarCross RefCross Ref
  10. R. Mallipeddi, P. Suganthan, Q. Pan, and M. Tasgetiren. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing, 11(2):1679--1696, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. VanderPlas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825--2830, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. S. Rao. Engineering optimization: theory and practice. John Willey & Sons, New Jersey, 2009.Google ScholarGoogle Scholar
  13. R. Storn and K. Price. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4):341--359, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. X.-S. Yang. Appendix A: Test problems in optimization. In X.-S. Yang, editor, Engineering Optimization: An Introduction with Metaheuristic Applications, pages 261--266. John Wiley & Sons, Inc., Hoboken, NJ, USA, 2010.Google ScholarGoogle ScholarCross RefCross Ref

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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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