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dc.contributor.authorCrawford B.
dc.contributor.authorSoto R.
dc.contributor.authorMonfroy E.
dc.contributor.authorPalma W.
dc.contributor.authorCastro C.
dc.contributor.authorParedes F.
dc.date.accessioned2020-09-02T22:15:41Z
dc.date.available2020-09-02T22:15:41Z
dc.date.issued2013
dc.identifier10.1016/j.eswa.2012.09.013
dc.identifier.citation40, 5, 1690-1695
dc.identifier.issn09574174
dc.identifier.urihttps://hdl.handle.net/20.500.12728/4155
dc.descriptionA Constraint Satisfaction Problem is defined by a set of variables and a set of constraints, each variable has a nonempty domain of possible values. Each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. A solution of the problem is defined by an assignment of values to some or all of the variables that does not violate any constraints. To solve an instance, a search tree is created and each node in the tree represents a variable of the instance. The order in which the variables are selected for instantiation changes the form of the search tree and affects the cost of finding a solution. In this paper we explore the use of a Choice Function to dynamically select from a set of variable ordering heuristics the one that best matches the current problem state in order to show an acceptable performance over a wide range of instances. The Choice Function is defined as a weighted sum of process indicators expressing the recent improvement produced by the heuristic recently used. The weights are determined by a Particle Swarm Optimization algorithm in a multilevel approach. We report results where our combination of strategies outperforms the use of individual strategies. © 2012 Elsevier Ltd. All rights reserved.
dc.language.isoen
dc.subjectCombinatorial optimization
dc.subjectConstraints satisfaction
dc.subjectHyperheuristics
dc.subjectParticle Swarm
dc.subjectBest match
dc.subjectChoice function
dc.subjectConstraint satisfaction problems
dc.subjectConstraints satisfaction
dc.subjectHyper-heuristics
dc.subjectHyperheuristic
dc.subjectMultilevel approach
dc.subjectParameter-tuning
dc.subjectParticle swarm
dc.subjectParticle swarm optimization algorithm
dc.subjectProcess indicators
dc.subjectSearch trees
dc.subjectVariable ordering heuristics
dc.subjectWeighted Sum
dc.subjectAlgorithms
dc.subjectCombinatorial optimization
dc.subjectForestry
dc.subjectGenetic programming
dc.subjectParticle swarm optimization (PSO)
dc.subjectAlgorithms
dc.subjectGenetic Engineering
dc.subjectHeuristic Methods
dc.subjectOptimization
dc.subjectProblem Solving
dc.subjectRestraints
dc.titleParameter tuning of a choice-function based hyperheuristic using Particle Swarm Optimization
dc.typeArticle


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