per-instance algorithm selection and Automatic algorithm Configuration have recently gained important interests. However, these approaches face many limitations. For instance, the performance of these methods is deepl...
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ISBN:
(纸本)9781450367486
per-instance algorithm selection and Automatic algorithm Configuration have recently gained important interests. However, these approaches face many limitations. For instance, the performance of these methods is deeply influenced by factors like the accuracy of the underlying prediction model, features space correlation, incomplete performance space for new instances, instances sampling and many others. In this paper, an effort to address such limitations is described. Indeed, we propose a cooperative architecture, labeled as the "SAPIAS" concept, composed of a self-adaptive online algorithmselection system and an offline Automatic algorithm Configuration system, working together in order to deliver the most accurate performance. Additionally, SAPIAS is proposed as a methodic concept that the metaheuristics community might adopt to fill in the gap between theory and practice in the field, by providing for theoreticians the ability to continuously analyze the evolution of the problems characteristics and the behavior of the solving techniques as well as providing a ready to use solving framework for practitioners.
We define a distance function on propositional formulas in CNF as a measure of non-isomorphism of formulas: the larger the distance between two formulas is, the further they are from being isomorphic. This distance in...
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We define a distance function on propositional formulas in CNF as a measure of non-isomorphism of formulas: the larger the distance between two formulas is, the further they are from being isomorphic. This distance induces a metric on isomorphism classes of formulas. We show how this distance can be used for
SAT
solving, namely for per-instance algorithm selection where there is a “portfolio” of
SAT
solvers and there is a “meta-solver” that chooses a solver from the portfolio for a given input formula.
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