MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Chivilikhin:2013:GECCO,
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author = "Daniil Chivilikhin and Vladimir Ulyantsev",
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title = "{MuACOsm}: a new mutation-based ant colony
optimization algorithm for learning finite-state
machines",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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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",
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isbn13 = "978-1-4503-1963-8",
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pages = "511--518",
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keywords = "genetic algorithms, genetic programming",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2463372.2463440",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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