On the complexity of choosing the branching literal in DPLL
Artificial Intelligence
A new quantum behaved particle swarm optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Search heuristics for constraint-aided embodiment design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
An approach for dynamic split strategies in constraint solving
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
A hybrid AC3-tabu search algorithm for solving Sudoku puzzles
Expert Systems with Applications: An International Journal
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A 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.