Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3067695.3076041 | GECCO (Companion) |
Keywords | Field | DocType |
Genetic Algorithms, Hybridization, Constraint Handling, Combinatorial Optimization, Modelling, Meta-heuristics | Population,Heuristic,Mathematical optimization,Computer science,Constraint programming,Combinatorial optimization,Artificial intelligence,Genetic algorithm,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Lawrenson | 1 | 0 | 0.34 |
| Tommaso Urli | 2 | 79 | 8.66 |
| Philip Kilby | 3 | 117 | 9.89 |