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2017 | 15 | 1 | 261-280
Tytuł artykułu

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Wydawca
Czasopismo
Rocznik
Tom
15
Numer
1
Strony
261-280
Opis fizyczny
Daty
wydano
2017-01-01
otrzymano
2016-09-07
zaakceptowano
2017-01-05
online
2017-03-19
Twórcy
autor
  • Dept. of Computer Science – IN3, Open University of Catalonia, Castelldefels,, lcalvetl@uoc.edu
  • Dept. of Computer Science – IN3, Open University of Catalonia, Castelldefels,, jde_armasa@uoc.edu
autor
  • Dept. of Computer Science – IN3, Open University of Catalonia, Castelldefels,, dmasipr@uoc.edu
  • Dept. of Computer Science – IN3, Open University of Catalonia, Castelldefels,, ajuanp@uoc.edu
Bibliografia
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.doi-10_1515_math-2017-0029
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