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Towards a Better Basis Search through a Surrogate Model-Based Epistasis Minimization for Pseudo-Boolean Optimization

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Listed:
  • Yong-Hoon Kim

    (Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

  • Yourim Yoon

    (Department of Computer Engineering, College of Information Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Korea)

  • Yong-Hyuk Kim

    (School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

Abstract

Epistasis, which indicates the difficulty of a problem, can be used to evaluate the basis of the space in which the problem lies. However, calculating epistasis may be challenging as it requires all solutions to be searched. In this study, a method for constructing a surrogate model, based on deep neural networks, that estimates epistasis is proposed for basis evaluation. The proposed method is applied to the Variant-OneMax problem and the N K -landscape problem. The method is able to make successful estimations on a similar level to basis evaluation based on actual epistasis, while significantly reducing the computation time. In addition, when compared to the epistasis-based basis evaluation, the proposed method is found to be more efficient.

Suggested Citation

  • Yong-Hoon Kim & Yourim Yoon & Yong-Hyuk Kim, 2020. "Towards a Better Basis Search through a Surrogate Model-Based Epistasis Minimization for Pseudo-Boolean Optimization," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1287-:d:394344
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    References listed on IDEAS

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    1. Melo, A.P. & Cóstola, D. & Lamberts, R. & Hensen, J.L.M., 2014. "Development of surrogate models using artificial neural network for building shell energy labelling," Energy Policy, Elsevier, vol. 69(C), pages 457-466.
    2. Edward D. Weinberger, 1996. "NP Completeness of Kauffman's N-k Model, A Tuneable Rugged Fitness Landscape," Working Papers 96-02-003, Santa Fe Institute.
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