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- W2048913209 abstract "Guided Local Search (GLS), which is a simple meta-heuristic with many successful applications, has lambda as the only parameter to tune. There has been no attempt to automatically tune this parameter, resulting in a parameterless GLS. Such a result is a very practical objective to facilitate the use of meta-heuristics for end- users (e.g. practitioners and researchers). In this paper, we propose a novel parameter tuning approach by using Genetic Programming (GP). GP is employed to evolve an optimal formula that GLS can use to dynamically compute lambda as a function of instance-dependent characteristics. Computational experiments on the travelling salesman problem demonstrate the feasibility and effectiveness of this approach, producing parameterless formulae with which the performance of GLS is competitive (if not better) than the standard GLS." @default.
- W2048913209 created "2016-06-24" @default.
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- W2048913209 date "2012-06-01" @default.
- W2048913209 modified "2023-10-16" @default.
- W2048913209 title "Off-line parameter tuning for Guided Local Search using Genetic Programming" @default.
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- W2048913209 doi "https://doi.org/10.1109/cec.2012.6256155" @default.
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