MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines

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@InProceedings{Chivilikhin:2013:GECCO,
  author =       "Daniil Chivilikhin and Vladimir Ulyantsev",
  title =        "{MuACOsm}: a new mutation-based ant colony
                 optimization algorithm for learning finite-state
                 machines",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "511--518",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463440",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper we present MuACOsm, a new method of
                 learning Finite-State Machines (FSM) based on Ant
                 Colony Optimisation (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. The goal is to maximise the given fitness
                 function, which is defined on the set of all FSMs with
                 given parameters. The new algorithm is compared with
                 evolutionary algorithms and a genetic programming
                 related approach on the well-known Artificial Ant
                 problem.",
  notes =        "Also known as \cite{2463440} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",
}

Genetic Programming entries for Daniil Chivilikhin Vladimir Ulyantsev

Citations