Learning Finite-State Machines: Conserving Fitness Function Evaluations by Marking Used Transitions

This paper is dedicated to the problem of learning finite-state machines (FSMs), which plays a key role in automata-based programming. Metaheuristic algorithms commonly applied to this problem often use FSM mutations (small changes in the FSM structure) for solution construction. Most of them do not employ the specifics of FSMs in their work. We propose a new simple method for improving performance of these algorithms. The basic idea is to mark those transitions of FSMs that were used during fitness evaluation. Then, if a FSM mutation changes a transition that was not used in fitness evaluation, the fitness function value need not be calculated for the mutated FSM. This observation allows to conserve fitness evaluations, which often have high computational costs. The proposed method has been incorporated into several traditional and recent FSM learning algorithms based on evolutionary strategies, genetic algorithms and ant colony optimization. Experimental results are reported showing that the new method significantly improves performance of two methods based on evolutionary strategies and ant colony optimization.