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International Conference on the Applications of Evolutionary Computation

EvoApplications 2018: Applications of Evolutionary Computation pp 578–593Cite as

Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

In many industrial problem domains, when faced with a combinatorial optimisation problem, a “good enough, quick enough” solution to a problem is often required. Simple heuristics often suffice in this case. However, for many domains, a simple heuristic may not be available, and designing one can require considerable expertise. Noting that a wide variety of problems can be represented as graphs, we describe a system for the automatic generation of constructive heuristics in the form of Python programs by mean of grammatical evolution. The system can be applied seamlessly to different graph-based problem domains, only requiring modification of the fitness function. We demonstrate its effectiveness by generating heuristics for the Travelling Salesman and Multi-Dimensional Knapsack problems. The system is shown to be better or comparable to human-designed heuristics in each domain. The generated heuristics can be used ‘out-of-the-box’ to provide a solution, or to augment existing hyper-heuristic algorithms with new low-level heuristics.

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Notes

  1. 1.

    https://github.com/PonyGE/PonyGE2.

  2. 2.

    http://people.brunel.ac.uk/mastjjb/jeb/info.html.

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Author information

Authors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Christopher Stone, Emma Hart & Ben Paechter

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  1. Christopher Stone
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  2. Emma Hart
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  3. Ben Paechter
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Correspondence to Christopher Stone .

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Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Stone, C., Hart, E., Paechter, B. (2018). Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_40

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_40

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