Learning Finite-State Machines with Ant Colony Optimization
In this paper we present a new method of learning Finite- State Machines (FSM) with the specified value of a given fitness function, which is based on an Ant Colony Optimization algorithm (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM and the goal is to maximize the given fitness function, which is defined on the set of all FSMs with given parameters. Comparison of the new algorithm and a genetic algorithm (GA) on benchmark problems shows that the new algorithm either outperforms GA or works just as well.
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