Hybrid Evolutionary Code Generation Optimizing Both Functional Form and Parameter Values

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@InProceedings{Courte:2007:ANNIE,
  author =       "Dale E. Courte",
  title =        "Hybrid Evolutionary Code Generation Optimizing Both
                 Functional Form and Parameter Values",
  booktitle =    "ANNIE 2007, Intelligent Engineering Systems through
                 Artificial Neural Networks",
  year =         "2007",
  editor =       "Cihan H. Dagli",
  volume =       "17",
  address =      "St. Louis, MO, USA",
  note =         "Part III: Evolutionary Computation",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  DOI =          "doi:10.1115/1.802655.paper35",
  abstract =     "Evolutionary computation (EC) is an effective tool in
                 the optimisation of complex systems. It is desirable to
                 model such a system with appropriate computer commands
                 and parameter settings. Automated determination of both
                 commands and settings, based on observed system
                 behaviour, is a desirable goal.

                 Of the many forms of evolutionary computation, one
                 recently developed discipline is that of grammatical
                 evolution (GE). This approach can evolve executable
                 functions in any computer language that can be
                 represented in BNF form. The ability to synthesise
                 arbitrary functions from a formal grammar is an
                 attractive alternative to the expression tree
                 generation of the more common genetic programming (GP)
                 approach. However, the GE approach may not be ideal for
                 the optimisation of any real-valued parameters of the
                 functions generated. This work combines the use of
                 grammatical evolution for function synthesis with the
                 use of evolutionary programming (EP) to optimise the
                 parameters (constants) required by the synthesised
                 functions. These two evolutionary processes combine to
                 explore a rich and complex search space of functional
                 forms and floating point values. A prototype system is
                 implemented and applied to the problem of function
                 approximation.",
}

Genetic Programming entries for Dale E Courte

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