Mark Lawrenson
Tommaso Urli
ABSTRACT
Two major challenges are presented when applying genetic algorithms (GAs) to constrained optimisation problems: modelling and constraint handling. The field of constraint programming (CP) has enjoyed extensive research in both of these areas. CP frameworks have been devised which allow arbitrary problems to be readily modelled, and their constraints handled efficiently. Our work aims to combine the modelling and constraint handling of a state-of-the-art CP framework with the efficient population-based search of a GA. We present a new general hybrid CP / GA framework which can be used to solve any constrained optimisation problem that can be expressed using the language of constraints. The efficacy of this framework as a general heuristic for constrained optimisation problems is demonstrated through experimental results on a variety of classical combinatorial optimisation problems commonly found in the literature.
- Lawrence Davis. 1991. Handbook of genetic algorithms. (1991).Google Scholar
- Francesca Rossi, Peter Van Beek, and Toby Walsh. 2006. Handbook of constraint programming. Elsevier. Google ScholarDigital Library
- KumaraSastry, David E Goldberg, and Graham Kendall. 2014. Genetic algorithms. In Search methodologies. Springer, 93--117.Google Scholar
- Tommaso Urli, Jana Brotánková, Philip Kilby, and Pascal Van Hentenryck. 2016. Intelligent Habitat Restoration Under Uncertainty.. In AAAI. 3908--3914. Google ScholarDigital Library
- Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, and Christian Prins. 2013. A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Computers & operations research 40, 1 (2013), 475--489. Google ScholarDigital Library
- Junhua Wu, Slava Shekh, Nataliia Y Sergiienko, Benjamin S Cazzolato, Boyin Ding, Frank Neumann, and Markus Wagner. 2016. Fast and effective optimisation of arrays of submerged wave energy converters. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference. ACM, 1045--1052. Google ScholarDigital Library
Index Terms
- A model-based genetic algorithm framework for constrained optimisation problems
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