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A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems

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Abstract

Constraint satisfaction problems represent an important topic of research due to their multiple applications in various areas of study. The most common way to solve this problem involves the use of heuristics that guide the search into promising areas of the space. In this article, we present a novel way to combine the strengths of distinct heuristics to produce solution methods that perform better than such heuristics on a wider range of instances. The methodology proposed produces neural networks that represent hyper-heuristics for variable ordering in constraint satisfaction problems. These neural networks are generated and trained by running a genetic algorithm that has the task of evolving the topology of the networks and some of their learning parameters. The results obtained suggest that the produced neural networks represent a feasible alternative for coding hyper-heuristics that control the use of different heuristics in such a way that the cost of the search is minimized.

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Notes

  1. http://www.cril.univ-artois.fr/~lecoutre/research/benchmarks/geom.tgz.

  2. http://www.cril.univ-artois.fr/~lecoutre/research/benchmarks/composed-25-10-20.tgz.

  3. http://www.cril.univ-artois.fr/~lecoutre/research/benchmarks/jobShop-e0ddr1.tgz.

  4. http://www.cril.univ-artois.fr/~lecoutre/research/benchmarks/rlfapScens.tgz.

  5. http://arxiv.org/pdf/0902.2362v1.

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Acknowledgments

This research was supported in part by ITESM Strategic Project PRY075, ITESM Research Group with Strategic Focus in Intelligent Systems and CONACyT Basic Science Projects under Grants 99695 and 241461.

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  1. José Carlos Ortiz-Bayliss

    Present address: National School of Engineering and Science, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, C.P. 64849, Monterrey, Mexico

Authors and Affiliations

  1. Automated Scheduling, Optimisation and Planning (ASAP) Research Group School of Computer Science, Jubilee Campus, University of Nottingham, Nottingham, NG8 1BB, UK

    José Carlos Ortiz-Bayliss

  2. National School of Engineering and Science, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, C.P. 64849, Monterrey, Mexico

    Hugo Terashima-Marín & Santiago Enrique Conant-Pablos

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  1. José Carlos Ortiz-Bayliss
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  2. Hugo Terashima-Marín
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  3. Santiago Enrique Conant-Pablos
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Correspondence to José Carlos Ortiz-Bayliss.

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Conflict of interest

José Carlos Ortiz-Bayliss, Hugo Terashima-Marín, and Santiago Enrique Conant-Pablos declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human or animal subjects performed by the any of the authors.

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Ortiz-Bayliss, J.C., Terashima-Marín, H. & Conant-Pablos, S.E. A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems. Cogn Comput 8, 429–441 (2016). https://doi.org/10.1007/s12559-015-9368-2

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