Model Representation and Cooperative Coevolution for Finite-State Machine Evolution

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@InProceedings{Dick:2014:CEC,
  title =        "Model Representation and Cooperative Coevolution for
                 Finite-State Machine Evolution",
  author =       "Grant Dick and Xin Yao",
  pages =        "2700--2707",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, FSM,
                 Evolutionary programming, Coevolutionary systems,
                 Coevolution and collective behaviour",
  DOI =          "doi:10.1109/CEC.2014.6900622",
  abstract =     "The use and search of finite-state machine (FSM)
                 representations has a long history in evolutionary
                 computation. The flexibility of Mealy-style and
                 Moore-style FSMs is traded against the large number of
                 parameters required to encode machines with many states
                 and/or large output alphabets. Recent work using Mealy
                 FSMs on the Tartarus problem has shown good performance
                 of the resulting machines, but the evolutionary search
                 is slower than for other representations. The aim of
                 this paper is two-fold: first, a comparison between
                 Mealy and Moore representations is considered on two
                 problems, and then the impact of cooperative
                 coevolution on FSM evolutionary search is examined. The
                 results suggest that the search space of Moore-style
                 FSMs may be easier to explore through evolutionary
                 search than the search space of an equivalent-sized
                 Mealy FSM representation. The results presented also
                 suggest that the tested cooperative coevolutionary
                 algorithms struggle to appropriately manage the
                 non-separability present in FSMs, indicating that new
                 approaches to cooperative coevolution may be needed to
                 explore FSMs and similar graphical structures.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Grant Dick Xin Yao

Citations