Creating adaptive sequences with genetic algorithms to reach a certain state in a non-deterministic FSM

This paper aims to construct an evolutionary system, based on genetic algorithms, to solve the problem of univocally reaching a target state in a non-deterministic Finite State Machine. Our approach proposes the creation of an adaptive sequence, which is a tree of input and outputs that contains the possible behaviors of the non-deterministic Finite State Machine, through a Genetic Algorithm. Essentially, we will characterize the DNA of the individuals as an adaptive sequence and allow the population to evolve until a solution is found. To assure the validity of our approach, we compare it with other methodologies such as hillclimbing and random. We show that the Genetic Algorithm obtains a higher rate of success in creating the adaptive sequences.