A General Framework Based on Machine Learning for Algorithm Selection in Constraint Satisfaction Problems

A General Framework Based on Machine Learning for Algorithm Selection in Constraint Satisfaction Problems

Many of the works conducted on algorithm selection strategies—methods that choose a suitable solving method for a particular problem—start from scratch since only a few investigations on reusable components of such methods are found in the literature. Additionally, researchers might unintentionally...

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Bibliographic Details
Main Authors: José C. Ortiz-Bayliss, Ivan Amaya, Jorge M. Cruz-Duarte, Andres E. Gutierrez-Rodriguez, Santiago E. Conant-Pablos, Hugo Terashima-Marín
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Applied Sciences
Subjects:
algorithm selection
machine learning
constraint satisfaction
heuristic
Online Access:https://www.mdpi.com/2076-3417/11/6/2749

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