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Continuous Optimization Based on a Hybridization of Differential Evolution with K-means

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Book cover Advances in Artificial Intelligence -- IBERAMIA 2014 (IBERAMIA 2014)

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Abstract

This paper presents a hybrid algorithm between Differential Evolution (DE) and K-means for continuous optimization. This algorithm includes the same operators of the original version of DE but works over groups previously created by the k-means algorithm, which helps to obtain more diversity in the population and skip local optimum values. Results over a large set of test functions were compared with results of the original version of Differential Evolution (DE/rand/1/bin strategy) and the Particle Swarm Optimization algorithm. The results shows that the average performance of the proposed algorithm is better than the other algorithms in terms of the minimum fitness function value reached and the average number of fitness function evaluations required to reach the optimal value. These results are supported by Friedman and Wilcoxon signed test, with a 95% significance.

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Authors and Affiliations

  1. Universidad del Cauca, Popayán, Colombia

    Luz-Marina Sierra, Carlos Cobos & Juan-Carlos Corrales

Authors
  1. Luz-Marina Sierra
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  2. Carlos Cobos
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  3. Juan-Carlos Corrales
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Corresponding author

Correspondence to Carlos Cobos .

Editor information

Editors and Affiliations

  1. Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Ana L.C. Bazzan

  2. Pontifica Universidad Católica (PUC), Santiago de Chile, Chile

    Karim Pichara

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Sierra, LM., Cobos, C., Corrales, JC. (2014). Continuous Optimization Based on a Hybridization of Differential Evolution with K-means. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-12027-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12026-3

  • Online ISBN: 978-3-319-12027-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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