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This work is focused on the Enumeration phase of Constraint Programming to solve Constraint Satisfaction Problems, an enumeration strategy is constituted by a variable selection heuristic and a value selection heuristic. A suitable definition and use of the enumeration strategy can strongly improve the resolution process. In order to select the enumeration strategies dynamically here we present a...
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.
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