Abstract
Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, rely on a set of domain-specific low-level heuristics at lower levels. For some domains, there is a lack of available heuristics, while for novel problems, no heuristics might exist. We address this issue by introducing a novel method, applicable in multiple domains, that constructs new low-level heuristics for a domain. The method uses grammatical evolution to construct iterated local search heuristics: it can be considered cross-domain in that the same grammar can evolve heuristics in multiple domains without requiring any modification, assuming that solutions are represented in the same form. We evaluate the method using benchmarks from the travelling-salesman (TSP) and multi-dimensional knapsack (MKP) domain. Comparison to existing methods demonstrates that the approach generates low-level heuristics that outperform heuristic methods for TSP and are competitive for MKP.
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Notes
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Using the R package TSPLIB.
- 3.
We do not provide statistical significance information as the PSO results, which are reported directly from [3], use a population based approach and vastly different number of evaluations.
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Stone, C., Hart, E., Paechter, B. (2018). On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_14
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