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

Stone, Christopher; Hart, Emma; Paechter, Ben

Authors

Christopher Stone



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.

Citation

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 Applications of Evolutionary Computation (578-593). https://doi.org/10.1007/978-3-319-77538-8_40

Conference Name 21st International Conference, EvoApplications 2018
Conference Location Parma, Italy
Start Date Apr 4, 2018
End Date Apr 6, 2018
Acceptance Date Jan 3, 2018
Online Publication Date Mar 8, 2018
Publication Date 2018
Deposit Date Aug 20, 2018
Electronic ISSN 1611-3349
Volume 10784
Pages 578-593
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743
Book Title Applications of Evolutionary Computation
ISBN 9783319775371; 9783319775388
DOI https://doi.org/10.1007/978-3-319-77538-8_40
Keywords heuristics, grammatical evolution, combinatorial optimisation
Public URL http://researchrepository.napier.ac.uk/Output/1281618