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| Content Provider | ACM Digital Library |
|---|---|
| Author | Hart, Emma Sim, Kevin |
| Abstract | Hyper-heuristic methods for solving vehicle routing problems (VRP) have proved promising on a range of data. The vast majority of approaches apply selective hyper-heuristic methods that iteratively choose appropriate heuristics from a fixed set of pre-defined low-level heuristics to either build or perturb a candidate solution. We propose a novel hyper-heuristic called GP-MHH that operates in two stages. The first stage uses a novel Genetic Programming (GP) approach to evolve high quality constructive heuristics; these can be used with any existing method that relies on a candidate solution(s) as its starting point. In the second stage, a perturbative hyper-heuristic is applied to candidate solutions created from the new heuristics. The new constructive heuristics are shown to outperform existing low-level heuristics. When combined with a naive perturbative hyper-heuristic they provide results which are both competitive with known optimal values and outperform a recent method that also designs new heuristics on some standard benchmarks. Finally, we provide results on a set of rich VRPs, showing the generality of the approach. |
| Starting Page | 1093 |
| Ending Page | 1100 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450342063 |
| DOI | 10.1145/2908812.2908942 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-20 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Vrp Genetic programming Hyper-heuristic |
| Content Type | Text |
| Resource Type | Article |
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