Learning Finite-State Machines with Classical and Mutation-Based Ant Colony Optimization: Experimental Evaluation

Abstract

The problem of learning finite-state machines (FSM) is tackled by three Ant Colony Optimization (ACO) algorithms. The first two classical ACO algorithms are based on the classical ACO combinatorial problem reduction, where nodes of the ACO construction graph represent solution components, while full solutions are built by the ants in the process of foraging. The third recently introduced mutation-based ACO algorithm employs another problem mapping, where construction graph nodes represent complete solutions. Here, ants travel between solutions to find the optimal one. In this paper we try to take a step back from the mutation-based ACO to find out if classical ACO algorithms can be used for learning FSMs. It was shown that classical ACO algorithms are inefficient for the problem of learning FSMs in comparison to the mutation-based ACO algorithm.