Test-based extended finite-state machines induction with evolutionary algorithms and ant colony optimization

Chivilikhin, Daniil; Ulyantsev, Vladimir; Tsarev, Fedor

doi:10.1145/2330784.2330883

Cited by 4 publications

(4 citation statements)

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“…Another algorithm we consider is the recently proposed FSM learning method [3], [11], [16] called MuACOsm, which is based on ant colony optimization [12]. The main idea of the method is to represent the search space in the form of a directed graph.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…systems that may react differently to the same input events depending on the history of interactions in the past. An example of such a system is a rather simple alarm clock [1], [3]. The key advantage of automata-based programming over traditional programming paradigms is that automata-based programs can be automatically verified [4] using model checking [5].…”

Section: Introductionmentioning

confidence: 99%

“…There are two major ways to define a fitness function: based on comparing the FSM's behavior to a standard recorded in test examples [3], [6], [7] or based on modeling in some environment [8], [9]. Both of these ways may lead to high computational costs of a single fitness function evaluation, which directly leads to an increased cost of building an optimal FSM.…”

Section: Introductionmentioning

confidence: 99%

“…An example of a work in which information about FSM transitions was used in the FSM learning algorithm is [10]. The proposed method was incorporated into an evolution strategy, a genetic algorithm and a recent FSM learning method MuACOsm [3], [11] based on ant colony optimization [12]. Experimental results describing the influence of the proposed method on the algorithms' performance are reported.…”

Section: Introductionmentioning

confidence: 99%

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

Chivilikhin

Ulyantsev

2013

2013 12th International Conference on Machine Learning and Applications

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This paper is dedicated to the problem of learning finite-state machines (FSMs), which plays a key role in automatabased 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.

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Section: Ant Colony Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%