DocumentCode :
2815587
Title :
Off-line parameter tuning for Guided Local Search using Genetic Programming
Author :
Alsheddy, A. ; Kampouridis, Michael
Author_Institution :
Comput. & Inf. Sci. Coll., Imam Muhammad Ibn Saud Islamic Univ., Riyadh, Saudi Arabia
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
5
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.
Keywords :
genetic algorithms; search problems; travelling salesman problems; genetic programming; guided local search; instance-dependent characteristics; metaheuristic; offline parameter tuning; parameter tuning approach; parameterless GLS; travelling salesman problem; Educational institutions; Equations; Mathematical model; Optimization; Search problems; Standards; Tuning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
Type :
conf
DOI :
10.1109/CEC.2012.6256155
Filename :
6256155
Link To Document :
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