Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics

Authors:
Carlos García-Martínez;Manuel Lozano
Affiliations:
University of Córdoba, Department of Computing and Numerical Analysis, 14071, Córdoba, Spain;University of Granada, Department of Computer Science and Artificial Intelligence, 18071, Granada, Spain
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Fuzzy Set Theory and Applications; Guest Editors: Ferdinand Chovanec, Olga Nánásiová, Alexander Šostak
Year:
2010

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Abstract

Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search operator for three local search-based metaheuristics: random multi-start local search, iterated local search, and variable neighborhood search. These general-purpose models treat the objective function as a black box, allowing the search process to be context-independent. The results show that BLGA may provide an effective and efficient intensification, not only allowing these three metaheuristics to be enhanced, but also predicting successful applications in other local search-based algorithms. In addition, the empirical results reported here reveal relevant insights on the behavior of classical local search methods when they are performed as context-independent optimizers in these three well-known metaheuristics.