MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems

Bessière, Christian; Régin, Jean‐Charles

doi:10.1007/3-540-61551-2_66

Cited by 184 publications

(143 citation statements)

References 10 publications

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“…• For binary CSPs,for a long time it was considered that the Forward Checking algorithm (the filtering algorithms are triggered only when some variables are instantiated) was the most efficient one, but several studies showed that the systematic call of filtering algorithms after every modification is worthwhile (for instance see (Bessière and Régin, 1996)). All industrial solver vendors aim to solve real world applications and claim that the use of strong filtering algorithms is often essential.…”

Section: Power Of a Filtering Algorithmmentioning

confidence: 99%

Global Constraints and Filtering Algorithms

Régin¹

2004

Constraint and Integer Programming

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Constraint programming (CP) is mainly based on filtering algorithms; their association with global constraints is one of the main strengths of CP. This chapter is an overview of these two techniques. Some of the most frequently used global constraints are presented. In addition, the filtering algorithms establishing arc consistency for two useful constraints, the alldiff and the global cardinality constraints, are fully detailed. Filtering algorithms are also considered from a theoretical point of view: three different ways to design filtering algorithms are described and the quality of the filtering algorithms studied so far is discussed. A categorization is then proposed. Over-constrained problems are also mentioned and global soft constraints are introduced.

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Section: Power Of a Filtering Algorithmmentioning

confidence: 99%

Global Constraints and Filtering Algorithms

Régin¹

2004

Constraint and Integer Programming

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“…; init (X i ) denotes the set of nodes sharing an edge with the node X i (its initial neighbors). We de ne the set ; (X i ) as the current neighborhood of X i , namely, the neighbors remaining uninstantiated once a backtracking search procedure has instantiated 1 However, the techniques presented in the following of this paper can easily be extended to general non-binary constraint n e t works. the set Y = fX i1 : : : X i k g of variables, i.e., ; (X i ) = ; init (X i ) n Y .…”

Section: De Nitions and Notationsmentioning

confidence: 99%

“…Many works have studied the different levels of ltering that can be applied at each node of the search tree. In the following, we will use maintaining arc consistency (MAC) as our search algorithm 13,1].…”

Section: Introductionmentioning

confidence: 99%

Neighborhood-Based Variable Ordering Heuristics for the Constraint Satisfaction Problem

Bessière

Chmeiss

Saïs

2001

Principles and Practice of Constraint Programming — CP 2001

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Abstract. The usual way for solving constraint satisfaction problems is to use a backtracking algorithm. One of the key factors in its e ciency is the rule it will use to decide on which v ariable to branch next (namely, the variable ordering heuristics). In this paper, we attempt to give a general formulation of dynamic variable ordering heuristics that take i n to account the properties of the neighborhood of the variable. An empirical evaluation on random CSPs and a sample of real instances shows that the obtained heuristics can improve signi cantly the current b e s t o n e s .

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“…• dom/ddeg [3]: it selects first the variable having the minimal ratio between domain size and degree. The degree can be computed dynamically by the number of constraints linking a given variable to uninstantiate variables;…”

Section: Introductionmentioning

confidence: 99%

Weight-based Heuristics for Constraint Satisfaction and Combinatorial Optimization Problems

Huguet¹,

López²,

Karoui³

2012

J Math Model Algor

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In this paper, we propose mechanisms to improve instantiation heuristics by incorporating weighted factors on variables. The proposed weight-based heuristics are evaluated on several tree search methods such as chronological backtracking and discrepancy-based search for both constraint satisfaction and optimization problems. Experiments are carried out on random constraint satisfaction problems, car sequencing problems, and jobshop scheduling with time-lags, considering various parameter settings and variants of the methods. The results show that weighting mechanisms reduce the tree size and then speed up the solving time, especially for the discrepancy-based search method.

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MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems

Cited by 184 publications

References 10 publications

Global Constraints and Filtering Algorithms

Global Constraints and Filtering Algorithms

Neighborhood-Based Variable Ordering Heuristics for the Constraint Satisfaction Problem

Weight-based Heuristics for Constraint Satisfaction and Combinatorial Optimization Problems

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