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Differential Evolution Based on Learnable Evolution Model for Function Optimization

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Advances in Soft Computing (MICAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11835))

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Abstract

With the advance of technology, the generation of massive amounts of information grows every day, generating complex problems difficult to manage in an efficient way. Therefore, researchers have studied and modeled the way in which natural biological systems react and behave in certain situations, allowing to developed algorithms that exhibit a capacity to learn and/or adapt to new situations, obtaining better results than traditional approaches. In this article we present a new variant of the Differential Evolution (DE) algorithm inspired by the concept of the Learnable Evolution Model (LEM) to enhance the search capability through a selection mechanism based on machine learning to create a set of rules that allows the inferring of new candidates in the population that emerge not only the random scan. The proposed algorithm is tested and validated on a set of 23 bechmark test functions and its performance is compared with other metaheuristics. Results indicate that the proposed DE+LEM is competitive with other metaheuristic.

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Authors and Affiliations

  1. Universidad Autónoma del Estado de Méxio, Mexico City, Estado de México, Mexico

    Esteban Morales, Cristina Juárez & José Sanchéz

  2. Escuela Militar de Transmisiones, Mexico City, Mexico

    Edgar García

Authors
  1. Esteban Morales
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  2. Cristina Juárez
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  3. Edgar García
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  4. José Sanchéz
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Corresponding author

Correspondence to Esteban Morales .

Editor information

Editors and Affiliations

  1. Universidad Panamericana, Mexico City, Mexico

    Lourdes Martínez-Villaseñor

  2. Instituto Politecnico Nacional, Mexico, Mexico

    Ildar Batyrshin

  3. Universidad Veracruzana, Xalapa, Mexico

    Antonio Marín-Hernández

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Morales, E., Juárez, C., García, E., Sanchéz, J. (2019). Differential Evolution Based on Learnable Evolution Model for Function Optimization. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_24

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  • DOI: https://doi.org/10.1007/978-3-030-33749-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33748-3

  • Online ISBN: 978-3-030-33749-0

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