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European Conference on Evolutionary Computation in Combinatorial Optimization

EvoCOP 2018: Evolutionary Computation in Combinatorial Optimization pp 67–84Cite as

Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10782))

Abstract

Automatic methods have been applied to find good heuristic algorithms to combinatorial optimization problems. These methods aim at reducing human efforts in the trial-and-error search for promising heuristic strategies. We propose a grammar-based approach to the automatic design of heuristics and apply it to binary quadratic programming. The grammar represents the search space of algorithms and parameter values. A solution is represented as a sequence of categorical choices, which encode the decisions taken in the grammar to generate a complete algorithm. We use an iterated F-race to evolve solutions and tune parameter values. Experiments show that our approach can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

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Notes

  1. 1.

    Beasley and MaxCut instances can be found in http://biqmac.uni-klu.ac.at.

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Author information

Authors and Affiliations

  1. Santa Catarina State University, Ibirama, SC, Brazil

    Marcelo de Souza

  2. Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

    Marcelo de Souza & Marcus Ritt

Authors
  1. Marcelo de Souza
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  2. Marcus Ritt
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Correspondence to Marcelo de Souza .

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Editors and Affiliations

  1. University of Lille, Lille, France

    Arnaud Liefooghe

  2. University of Manchester, Manchester, United Kingdom

    Manuel López-Ibáñez

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de Souza, M., Ritt, M. (2018). Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming. In: Liefooghe, A., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018. Lecture Notes in Computer Science(), vol 10782. Springer, Cham. https://doi.org/10.1007/978-3-319-77449-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-77449-7_5

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