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In Constraint Programming, selection of a variable and a value of its domain enumeration strategies are crucial for resolution performances. We propose to use a Choice Function for guiding enumeration: we exploit search process features to dynamically adapt a Constraint Programming solver in order to more efficiently solve Constraint Satisfaction Problems. The Choice Function provides guidance to...
In this work we propose a Choice Function for guiding Constraint Programming in the resolution of Constraint Satisfaction Problems. We exploit some search process features to select on the fly the Enumeration Strategy (Variable + Value Selection Heuristics) in order to more efficiently solve the problem at hand. The main novelty of our approach is that we reconfigure the search based solely on performance...
In this work we exploit search process features to dynamically adapt a constraint programming solver in order to more efficiently solve constraint satisfaction problems. The main novelty of our approach is that we reconfigure the searching or search process based solely on performance data gathered while solving the current problem. We report encouraging results where our combination of strategies...
Constraint Programming is a powerful paradigm for solving Combinatorial Problems. In this solver approach, Enumeration Strategies are crucial for resolution performances. In a previous work, we proposed a framework to reactively change strategies showing bad performances, and to use metabacktracks to restore better states when bad decisions were made. In this paper, we design and evaluate strategies...
In constraint programming, enumeration strategies are crucial for resolution performances. In this work, we model the known NP-complete problems Latin Square, Magic Square and Sudoku as a constraint satisfaction problems. We solve them with constraint programming comparing the performance of different variable and value selection heuristics in its enumeration phase.
In order to be able to solve any combinatorial optimization problem it seems that a good idea is to use both incomplete and complete techniques together. When problems are easy enough to allow searching for the optimal solution, complete techniques can be used. When problems become harder, incomplete techniques represent a good alternative in order to solve approximately the problem. Particularly,...
In the paper we describe a theoretical framework to model local search as the computation of a fixed point of functions. There are only few studies of theoretical frameworks for local search, this work allows one to simulate standard strategies used for local search and to easily design new strategies in a uniform framework. The use of this framework is illustrated through the description of Tabu...
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