Anytime automatic algorithm selection for knapsack

•New “Anytime” Framework for Automatic Algorithm Selection.•Application of the Anytime Automatic Algorithm Selection Framework to Knapsack.•3 Machine Learning Models proposed for the Problem.•Knapsack Instances, Features and Solvers Behavior publicly available. In this paper, we present a new approa...

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Veröffentlicht in:Expert systems with applications Jg. 158; S. 113613
Hauptverfasser: Huerta, Isaías I., Neira, Daniel A., Ortega, Daniel A., Varas, Vicente, Godoy, Julio, Asín-Achá, Roberto
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.11.2020
Elsevier BV
Schlagworte:
Algorithm selection problem
Algorithms
Anytime behaviour approach
Knapsack
Knapsack problem
Machine learning
Machine learning techniques
Solvers
Anytime behaviour approach
Machine learning techniques
Knapsack
Algorithm selection problem
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•New “Anytime” Framework for Automatic Algorithm Selection.•Application of the Anytime Automatic Algorithm Selection Framework to Knapsack.•3 Machine Learning Models proposed for the Problem.•Knapsack Instances, Features and Solvers Behavior publicly available. In this paper, we present a new approach for Automatic Algorithm Selection. In this new procedure, we feed the predictor of the best algorithm choice with a runtime limit for the solvers. Hence, the machine learning model should consider and learn from the Anytime Behavior of the solvers, together with features characterizing each instance. For this purpose, we propose a general Framework and apply it to the Knapsack problem. Thus, we created a large and diverse dataset of 15,000 instances, recorded the anytime behavior of 8 solvers on them and trained and tested three machine learning strategies, collecting the results for different machine learning algorithms. Our results show that, for the majority of the tuples <instance, time>, the solver that computes the best objective value can be predicted. We also make this data publicly available, as a challenge for the community to work in this problem and propose new and better machine learning models and solvers.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113613