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A RNN-Based Hyper-heuristic for Combinatorial Problems

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13222))

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Abstract

Designing efficient heuristics is a laborious and tedious task that generally requires a full understanding and knowledge of a given optimization problem. Hyper-heuristics have been mainly introduced to tackle this issue and are mostly relying on Genetic Programming and its variants. Many attempts in the literature have shown that an automatic training mechanism for heuristic learning is possible and can challenge human-based heuristics in terms of gap to optimality. In this work, we introduce a novel approach based on a recent work on Deep Symbolic Regression. We demonstrate that scoring functions can be trained using Recurrent Neural Networks to tackle a well-know combinatorial problem, i.e., the Multi-dimensional Knapsack. Experiments have been conducted on instances from the OR-Library and results show that the proposed modus operandi is an alternative and promising approach to human-based heuristics and classical heuristic generation approaches.

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Notes

  1. 1.

    http://people.brunel.ac.uk/mastjjb/jeb/orlib/mknapinfo.html.

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Authors and Affiliations

  1. Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Emmanuel Kieffer, Grégoire Danoy, Sébastien Varrette & Pascal Bouvry

  2. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Gabriel Duflo, Grégoire Danoy & Pascal Bouvry

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  1. Emmanuel Kieffer
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  2. Gabriel Duflo
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  3. Grégoire Danoy
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  4. Sébastien Varrette
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  5. Pascal Bouvry
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Correspondence to Emmanuel Kieffer .

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  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Leslie Pérez Cáceres

  2. Université du Littoral Côte d’Opale, Calais, France

    Sébastien Verel

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Kieffer, E., Duflo, G., Danoy, G., Varrette, S., Bouvry, P. (2022). A RNN-Based Hyper-heuristic for Combinatorial Problems. In: Pérez Cáceres, L., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2022. Lecture Notes in Computer Science, vol 13222. Springer, Cham. https://doi.org/10.1007/978-3-031-04148-8_2

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