Abstract
Some models using metaheuristics based in an “improvement of solutions” procedure, specifically Genetic Algorithms (GA), have been proposed previously to the linguistic summarization of numerical data (LDS). In the present work is proposed a new model for LDS based in Ant Colony Optimization (ACO), a metaheuristic that use a “construction of solution” procedure. Both models are compared in LDS over creep data. Results show how the ACO based model overcomes the measures of goodness of the final summary but fails to improve the results of the GA based model in relation to the diversity of the summary. Features of both models are considered to explain the results.
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Donis-Díaz, C.A., Bello, R., Kacprzyk, J. (2015). Using Ant Colony Optimization and Genetic Algorithms for the Linguistic Summarization of Creep Data. In: Angelov, P., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-11313-5_8
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DOI: https://doi.org/10.1007/978-3-319-11313-5_8
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