Evolutionary algorithms have been applied to a wide variety of domains with successful results, supported by the increase of computational resources. One of such domains is segmentation, the representation of a given curve by means of a series of linear models minimizing the representation error. This work analyzes the impact of the initialization method on the performance of a multiobjective evolutionary algorithm for this segmentation domain, comparing a random initialization with two different approaches introducing domain knowledge: a hybrid approach based on the application of a local search method and a novel method based on the analysis of the Pareto Front structure. © 2012 Springer-Verlag.
CITATION STYLE
Guerrero, J. L., Berlanga, A., & Molina, J. M. (2012). Initialization procedures for multiobjective evolutionary approaches to the segmentation issue. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7208 LNAI, pp. 452–463). https://doi.org/10.1007/978-3-642-28942-2_41
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