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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine pp 169–179Cite as

The Differential Evolution Algorithm with a Fuzzy Logic Approach for Dynamic Parameter Adjustment Using Benchmark Functions

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

Abstract

In this paper the main idea is to state that the use of fuzzy logic helps in the improvement of results in different optimization problems. For this particular paper we propose using the methodology of combining fuzzy logic with the differential evolution algorithm to perform experiments with a set of functions of the CEC2015, since these functions are more complicated than traditional benchmark functions.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Patricia Ochoa, Oscar Castillo & José Soria

Authors
  1. Patricia Ochoa
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  2. Oscar Castillo
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  3. José Soria
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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

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Ochoa, P., Castillo, O., Soria, J. (2020). The Differential Evolution Algorithm with a Fuzzy Logic Approach for Dynamic Parameter Adjustment Using Benchmark Functions. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_12

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