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Learning Finite-State Machines with Ant Colony Optimization

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Swarm Intelligence (ANTS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7461))

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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.

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Author information

Authors and Affiliations

  1. Mechanics and Optics, Saint-Petersburg National Research University of Information Technologies, Saint-Petersburg, Russia

    Daniil Chivilikhin & Vladimir Ulyantsev

Authors
  1. Daniil Chivilikhin
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  2. Vladimir Ulyantsev
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Editor information

Editors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, CoDE, Av. F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Marco Dorigo , Mauro Birattari  & Thomas Stützle ,  & 

  2. ALBCOM, Department Llenguatges i Sistemes Informatics, Universitat Politecnica de Catalunya, Jordi Girona 1-3, Omega 112 Campus Nord, 08034, Barcelona, Spain

    Christian Blum

  3. Instituto de Telecomunicações & Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Foras Armadas, 1649-026, Lisboa, Portugal

    Anders Lyhne Christensen

  4. Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    Andries P. Engelbrecht

  5. Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Roderich Groß

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Chivilikhin, D., Ulyantsev, V. (2012). Learning Finite-State Machines with Ant Colony Optimization. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2012. Lecture Notes in Computer Science, vol 7461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32650-9_27

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  • DOI: https://doi.org/10.1007/978-3-642-32650-9_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32649-3

  • Online ISBN: 978-3-642-32650-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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