A genetic algorithm for finite state machine inference

Abstract

This thesis tackles the problem of finite state machine inference. The objective of the problem is to synthesize a finite state machine that can mimic the target machine by passively inspecting the input/output of the target machinte. This work proposes a genetic algorithm for the problem. The experiments are carried out to compare the performance and the efficiency of the proposed algorithm. The result indicates that the proposed algorithm outperforms other methods. This work also gives an analysis of the algorithm in comparison with other algorithms. The analysis shows interesting issues in genetic algorithms such as introns and the linkage problem.