A binaryconstraintsatisfaction problem (BCSP) consists in determining an assignment of values to variables that is compatible with a set of constraints. The problem is called binary because the constraints involve o...
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A binaryconstraintsatisfaction problem (BCSP) consists in determining an assignment of values to variables that is compatible with a set of constraints. The problem is called binary because the constraints involve only pairs of variables. The BCSP is a cornerstone problem in constraint Programming (CP), appearing in a very wide range of real-world applications. In this work, we develop a new exact algorithm which effectively solves the BCSP by reformulating it as a k-clique problem on the underlying microstructure graph representation. Our new algorithm exploits the cutting-edge branching scheme of the stateof-the-art maximum clique algorithms combined with two filtering phases in which the domains of the variables are reduced. Our filtering phases are based on colouring techniques and on heuristically solving an associated boolean satisfiability (SAT) problem. In addition, the algorithm initialization phase performs a reordering of the microstructure graph vertices that produces an often easier reformulation to solve. We carry out an extensive computational campaign on a benchmark of almost 20 0 0 instances, encompassing numerous real and synthetic problems from the literature. The performance of the new algorithm is compared against four SAT-based solvers and three general purpose CP solvers. Our tests reveal that the new algorithm significantly outperforms all the others in several classes of BCSP instances. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )
This paper proposes an evolutionary approach for the composition of solutions in an incremental way. The approach is based on the metaphor of transitions in complexity discussed in the context of evolutionary biology....
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ISBN:
(纸本)0780393635
This paper proposes an evolutionary approach for the composition of solutions in an incremental way. The approach is based on the metaphor of transitions in complexity discussed in the context of evolutionary biology. Partially defined solutions interact and evolve into aggregations until a full solution for the problem at hand is found. The impact of the initial population on the outcome and the dynamics of the process is evaluated using the domain of binary constraint satisfaction problems.
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