Off-line parameter tuning for Guided Local Search using Genetic Programming

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...

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Bibliographic Details
Main Authors: Alsheddy, A., Kampouridis, M.
Format: Conference Proceeding
Language:English
Subjects:
Educational institutions
Equations
Mathematical model
Optimization
Search problems
Tuning
Online Access:Request full text
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Summary: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2012.6256155