GA and ILS for optimizing the size of NFA models
This work addresses the computational efficiency of grammatical inference for researchers in formal language theory, but it is incremental as it builds on existing SAT modeling techniques.
The paper tackled the problem of learning Nondeterministic Finite Automata (NFA) of a given size from samples by optimizing SAT instance sizes using hybrid models based on prefixes and suffixes, resulting in significant reductions in SAT instances and solving time, though with increased generation time.
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.