Abstract
The Clustering Search (*CS) has been proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process may keep representative solutions associated to different search subspaces. Although, recent applications have reached success in combinatorial optimisation problems, nothing new has arisen concerning diversification issues when population metaheuristics, as evolutionary algorithms, are being employed. In this work, recent advances in the *CS are commented and new features are proposed, including, the possibility of keeping population diversified for more generations.
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Costa, T.S., de Oliveira, A.C.M., Lorena, L.A.N. (2010). Advances in Clustering Search. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds) Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13161-5_29
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DOI: https://doi.org/10.1007/978-3-642-13161-5_29
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