Meta-learning-based system for solving logistic optimization problems

Alan Dávila de León; Eduardo Lalla-Ruiz; Belén Melián-Batista; J. Marcos Moreno-Vega

doi:10.1007/978-3-319-74718-7_41

Meta-learning-based system for solving logistic optimization problems

Alan Dávila de León^*, Eduardo Lalla-Ruiz, Belén Melián-Batista, J. Marcos Moreno-Vega

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2017
Subtitle of host publication	16th International Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017. Revised Selected Papers, Part I
Editors	Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Place of Publication	Cham
Publisher	Springer
Pages	339-346
Number of pages	8
ISBN (Electronic)	978-3-319-74718-7
ISBN (Print)	978-3-319-74717-0
DOIs	https://doi.org/10.1007/978-3-319-74718-7_41
Publication status	Published - 1 Jan 2018
Event	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 - Las Palmas de Gran Canaria, Spain Duration: 19 Feb 2017 → 24 Feb 2017 Conference number: 16 http://eurocast2017.fulp.ulpgc.es/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10671
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Abbreviated title	EUROCAST 2017
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	19/02/17 → 24/02/17
Internet address	http://eurocast2017.fulp.ulpgc.es/

Keywords

Logistic problems
Meta-heuristics
Meta-learning

Access to Document

10.1007/978-3-319-74718-7_41

Cite this

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 R. Moreno-Díaz, F. Pichler, & A. Quesada-Arencibia (Eds.), Computer Aided Systems Theory – EUROCAST 2017: 16th International Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017. Revised Selected Papers, Part I (pp. 339-346). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671). Springer. https://doi.org/10.1007/978-3-319-74718-7_41

de León, Alan Dávila ; Lalla-Ruiz, Eduardo ; Melián-Batista, Belén ; Moreno-Vega, J. Marcos. / Meta-learning-based system for solving logistic optimization problems. Computer Aided Systems Theory – EUROCAST 2017: 16th International Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017. Revised Selected Papers, Part I. editor / Roberto Moreno-Díaz ; Franz Pichler ; Alexis Quesada-Arencibia. Cham : Springer, 2018. pp. 339-346 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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de León, AD, Lalla-Ruiz, E, Melián-Batista, B & Moreno-Vega, JM 2018, Meta-learning-based system for solving logistic optimization problems. in R Moreno-Díaz, F Pichler & A Quesada-Arencibia (eds), Computer Aided Systems Theory – EUROCAST 2017: 16th International Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017. Revised Selected Papers, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10671, Springer, Cham, pp. 339-346, 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Las Palmas de Gran Canaria, Spain, 19/02/17. https://doi.org/10.1007/978-3-319-74718-7_41

Meta-learning-based system for solving logistic optimization problems. / de León, Alan Dávila; Lalla-Ruiz, Eduardo; Melián-Batista, Belén; Moreno-Vega, J. Marcos.

Computer Aided Systems Theory – EUROCAST 2017: 16th International Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017. Revised Selected Papers, Part I. ed. / Roberto Moreno-Díaz; Franz Pichler; Alexis Quesada-Arencibia. Cham : Springer, 2018. p. 339-346 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Meta-learning-based system for solving logistic optimization problems

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AU - Lalla-Ruiz, Eduardo

AU - Melián-Batista, Belén

AU - Moreno-Vega, J. Marcos

N1 - Conference code: 16

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N2 - 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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de León AD, Lalla-Ruiz E, Melián-Batista B, Moreno-Vega JM. Meta-learning-based system for solving logistic optimization problems. In Moreno-Díaz R, Pichler F, Quesada-Arencibia A, editors, Computer Aided Systems Theory – EUROCAST 2017: 16th International Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017. Revised Selected Papers, Part I. Cham: Springer. 2018. p. 339-346. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-74718-7_41

Meta-learning-based system for solving logistic optimization problems

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