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In this paper we present MuACOsm -a new method of learning Finite-State Machines (FSM) based on Ant Colony Optimization (ACO) and a graph representation of the search space. ... The new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem. ... MUTATION-BASED ACO FOR LEARN-ING FINITE-STATE MACHINES In this section we provide a full description of the new algorithm. First we describe the representation of the search space. ...doi:10.1145/2463372.2463440 dblp:conf/gecco/ChivilikhinU13 fatcat:pi426wm2tbevvnvl4bxbhsacsu
2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence
The problem of learning finite-state machines (FSM) is tackled by three Ant Colony Optimization (ACO) algorithms. ... Here, ants travel between solutions to find the optimal one. In this paper we try to take a step back from the mutationbased ACO to find out if classical ACO algorithms can be used for learning FSMs. ... The authors of this paper recently introduced a new method of learning FSMs called MuACOsm, which is based on ant colony optimization. ...doi:10.1109/brics-cci-cbic.2013.93 fatcat:euyevpcz45citmbr6alskeudlq
This paper is dedicated to the problem of learning finite-state machines (FSMs), which plays a key role in automatabased programming. ... The proposed method has been incorporated into several traditional and recent FSM learning algorithms based on evolutionary strategies, genetic algorithms and ant colony optimization. ... LEARNING FINITE-STATE MACHINES WITH MUTATION-BASED METAHEURISTICS In this paper we concentrate on Mealy finite-state machines. ...doi:10.1109/icmla.2013.111 dblp:conf/icmla/ChivilikhinU13 fatcat:isnch4nsfbcqxkj2wf3bwxeop4