Abstract
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. © 2012 Springer-Verlag.
Cite
CITATION STYLE
Chivilikhin, D., & Ulyantsev, V. (2012). Learning finite-state machines with ant colony optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7461 LNCS, pp. 268–275). https://doi.org/10.1007/978-3-642-32650-9_27
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