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http://hdl.handle.net/10609/70640
Title: Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
Author: Calvet Liñan, Laura  
Armas Adrián, Jésica de
Masip Rodó, David
Juan Pérez, Ángel Alejandro
Keywords: hybrid algorithms
combinatorial optimization
metaheuristics
machine learning
dynamic inputs
Issue Date: Mar-2017
Publisher: Open Mathematics
Citation: Calvet Liñan, L., de Armas Adrián, J., Masip Rodo, D. & Juan, A.A. (2017). "Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs". Open Mathematics, 15(1), 261-280. ISSN 2391-5455. doi: 10.1515/math-2017-0029
Abstract: This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.
Language: English
URI: http://hdl.handle.net/10609/70640
ISSN: 2391-5455MIAR
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