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

Laura Calvet, et al. "Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs." Open Mathematics 15.1 (2017): 261-280. <http://eudml.org/doc/287999>.

@article{LauraCalvet2017,
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.},
author = {Laura Calvet, Jésica de Armas, David Masip, Angel A. Juan},
journal = {Open Mathematics},
keywords = {Hybrid algorithms; Combinatorial optimization; Metaheuristics; Machine learning; Dynamic inputs; hybrid algorithms; combinatorial optimization; metaheuristics; machine learning; dynamic inputs},
language = {eng},
number = {1},
pages = {261-280},
title = {Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs},
url = {http://eudml.org/doc/287999},
volume = {15},
year = {2017},
}

TY - JOUR
AU - Laura Calvet
AU - Jésica de Armas
AU - David Masip
AU - Angel A. Juan
TI - Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
JO - Open Mathematics
PY - 2017
VL - 15
IS - 1
SP - 261
EP - 280
AB - 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.
LA - eng
KW - Hybrid algorithms; Combinatorial optimization; Metaheuristics; Machine learning; Dynamic inputs; hybrid algorithms; combinatorial optimization; metaheuristics; machine learning; dynamic inputs
UR - http://eudml.org/doc/287999
ER -