Off-line Parameter Tuning for Guided Local Search Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Alsheddy:2012:CEC,

• title = "Off-line Parameter Tuning for Guided Local Search Using Genetic Programming",
• author = "Abdullah Alsheddy and Michael Kampouridis",
• pages = "112--116",
• booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation",
• year = "2012",
• editor = "Xiaodong Li",
• month = "10-15 " # jun,
• DOI = "doi:10.1109/CEC.2012.6256155",
• address = "Brisbane, Australia",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Heuristics, metaheuristics and hyper-heuristics",
• 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.",
• notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Abdullah Alsheddy Michael Kampouridis

Citations