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Communication dans un congrès

GA and ILS for optimizing the size of NFA models

Abstract : Grammatical inference consists in learning a formal grammar (as a set of rewrite rules or a finite state machine). We are concerned with learning Nondeterministic Finite Automata (NFA) of a given size from samples of positive and negative words. NFA can naturally be modeled in SAT. The standard model [1] being enormous, we also try a model based on prefixes [2] which generates smaller instances. We also propose a new model based on suffixes and a hybrid model based on prefixes and suffixes. We then focus on optimizing the size of generated SAT instances issued from the hybrid models. We present two techniques to optimize this combination, one based on Iterated Local Search (ILS), the second one based on Genetic Algorithm (GA). Optimizing the combination significantly reduces the SAT instances and their solving time, but at the cost of longer generation time. We, therefore, study the balance between generation time and solving time thanks to some experimental comparisons, and we analyze our various model improvements.
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Communication dans un congrès
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Contributeur : Frédéric lardeux Connectez-vous pour contacter le contributeur
Soumis le : lundi 12 juillet 2021 - 16:14:31
Dernière modification le : lundi 14 novembre 2022 - 02:42:07
Archivage à long terme le : : mercredi 13 octobre 2021 - 19:25:23


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  • HAL Id : hal-03284541, version 1
  • ARXIV : 2107.05877



Frédéric Lardeux, Eric Monfroy. GA and ILS for optimizing the size of NFA models. The 8th International Conference on Metaheuristics and Nature Inspired Computing (META), Oct 2021, Marrakech, Morocco. ⟨hal-03284541⟩



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