# GA and ILS for optimizing the size of NFA models

arXiv: https://arxiv.org/abs/2107.05877 · HTML: https://mlsanity.com/paper/2107.05877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Taking advantage of a very simple property to efficiently infer NFAs](https://mlsanity.com/paper/2303.09311/) (2023)

## Abstract (arXiv)

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.