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Yet Another Local Search Method for Constraint Solving

Abstract : We propose a generic, domain-independent local search method called adaptive search for solving Constraint Satisfaction Problems (CSP). We design a new heuristics that takes advantage of the structure of the problem in terms of constraints and variables and can guide the search more precisely than a global cost function to optimize (such as for instance the number of violated constraints). We also use an adaptive memory in the spirit of Tabu Search in order to prevent stagnation in local minima and loops. This method is generic, can apply to a large class of constraints (e.g. linear and non-linear arithmetic constraints, symbolic constraints, etc) and naturally copes with over-constrained problems. Preliminary results on some classical CSP problems show very encouraging performances. 1
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Contributor : Daniel Diaz Connect in order to contact the contributor
Submitted on : Wednesday, February 8, 2012 - 4:22:42 PM
Last modification on : Wednesday, September 14, 2022 - 3:23:06 PM

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Philippe Codognet, Daniel Diaz. Yet Another Local Search Method for Constraint Solving. Stochastic Algorithms, Foundations and Applications, Dec 2001, Berlin, Germany. pp.73-90, ⟨10.1007/3-540-45322-9_5⟩. ⟨hal-00667941⟩



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