Abstract
Local Genetic Algorithms are search procedures designed in order to provide an effective local search. Several Genetic Algorithm models have recently been presented with this aim. In this paper we present a new Binary-coded Local Genetic Algorithm based on a Steady-State Genetic Algorithm with a crowding replacement method. We have compared a Multi-Start Local Search based on the Binary-Coded Local Genetic Algorithm with other instances of this metaheuristic based on Local Search Procedures presented in the literature. The results show that, for a wide range of problems, our proposal consistently outperforms the other local search approaches.
This research was supported by the Spanish MEC project TIN2005-08386-C05-01.
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García-Martínez, C., Lozano, M., Molina, D. (2006). A Local Genetic Algorithm for Binary-Coded Problems. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_20
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DOI: https://doi.org/10.1007/11844297_20
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