Abstract
The Algorithm Selection Problem seeks to select the most suitable algorithm for a given problem. For solving it, the algorithm selection systems have to face the so-called cold start. It concerns the disadvantage that arises in those cases where the system involved in the selection of the algorithm has not enough information to give an appropriate recommendation. Bearing that in mind, the main goal of this work is two-fold. On the one hand, a novel meta-learning-based approach that allows selecting a suitable algorithm for solving a given logistic problem is proposed. On the other hand, the proposed approach is enabled to work within cold start situations where a tree-structured hierarchy that enables to compare different metric dataset to identify a particular problem or variation is presented.
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This work has been partially funded by the European Regional Development Fund, the Spanish Ministry of Economy and Competitiveness (project TIN2015-70226-R).
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de León, A.D., Lalla-Ruiz, E., Melián-Batista, B., Moreno-Vega, J.M. (2018). Meta-Learning-Based System for Solving Logistic Optimization Problems. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_41
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DOI: https://doi.org/10.1007/978-3-319-74718-7_41
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