Autonomous tuning for constraint programming via artificial bee colony optimization

Ricardo Soto, Broderick Crawford, Felipe Mella, Javier Flores, Cristian Galleguillos, Sanjay Misra, Franklin Johnson, Fernando Paredes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Constraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings
EditorsSanjay Misra, Bernady O. Apduhan, Beniamino Murgante, Marina L. Gavrilova, David Taniar, Osvaldo Gervasi, Sanjay Misra, Carmelo Torre, Ana Maria A.C. Rocha
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319214030
StatePublished - 2015
Event15th International Conference on Computational Science and Its Applications, ICCSA 2015 - Banff, Canada
Duration: 22 Jun 201525 Jun 2015

Publication series

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


Conference15th International Conference on Computational Science and Its Applications, ICCSA 2015


  • Adaptive systems
  • Artificial intelligence
  • Metaheuristics
  • Optimization


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