GA and ILS for optimizing the size of NFA models

TL;DR

This paper explores methods to optimize the size of NFA models learned from data by using hybrid models and optimization algorithms like GA and ILS to reduce SAT instance complexity and solving time.

Contribution

It introduces a hybrid NFA modeling approach and applies GA and ILS to optimize SAT instance size, balancing generation and solving times.

Findings

Hybrid models reduce SAT instance size and solving time.

Optimization techniques significantly improve efficiency.

Trade-off between generation time and solving time is analyzed.

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.