When solving constraint satisfaction problems (CSPs) with stochastic search algorithms (SSAs) using the standard penalty function, it is not possible to show that there is no solution for a problem instance. In this paper we present a hybrid function that can be generally used in conjunction with SSAs to prove unsolvability without changing the search algorithms drastically. We use eight state-of-the-art algorithms to show the general usability of the new function. We compare the algorithms with and without the new penalty function and we test the scalability of the new function.
CITATION STYLE
Schoofs, L., & Naudts, B. (2000). Solving CSP instances beyond the phase transition using stochastic search algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1917, pp. 549–558). Springer Verlag. https://doi.org/10.1007/3-540-45356-3_54
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