Co-Evolution of Finite State Machines for Optimization: Promotion of Devices Which Search Globally

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{frey:2002a,
  author =       "Clemens Frey",
  title =        "Co-Evolution of Finite State Machines for
                 Optimization: Promotion of Devices Which Search
                 Globally",
  journal =      "International Journal of Computational Intelligence
                 and Applications",
  year =         "2002",
  volume =       "2",
  number =       "1",
  pages =        "1--16",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Co-evolution,
                 finite state machines, global search, robustness",
  ISSN =         "1469-0268",
  broken =       "http://www.mathematik.tu-darmstadt.de/~frey/",
  DOI =          "doi:10.1142/S1469026802000397",
  size =         "16 p.",
  abstract =     "In this work a co-evolutionary approach is used in
                 conjunction with Genetic Programming operators in order
                 to find certain transition rules for two-step discrete
                 dynamical systems. This issue is similar to the
                 well-known artificial-ant problem. We seek the dynamic
                 system to produce a trajectory leading from given
                 initial values to a maximum of a given spatial
                 functional. This problem is recast into the framework
                 of input-output relations for controllers, and the
                 optimisation is performed on program trees describing
                 input filters and finite state machines incorporated by
                 these controllers simultaneously. In the context of
                 Genetic Programming there is always a set of test cases
                 which has to be maintained for the evaluation of
                 program trees. These test cases are subject to
                 evolution here, too, so we employ a so-called
                 host-parasitoid model in order to evolve optimising
                 dynamical systems. Reinterpreting these systems as
                 algorithms for finding the maximum of a functional
                 under constraints, we have derived a paradigm for the
                 automatic generation of adapted optimisation algorithms
                 via optimal control. We provide numerical examples
                 generated by the GP-system MathEvEco. These examples
                 refer to key properties of the resulting strategies and
                 they include statistical evidence showing that for this
                 problem of system identification the co-evolutionary
                 approach is superior to standard Genetic Programming.",
}

Genetic Programming entries for Clemens Frey

Citations