Evolutionary Neural Trees for Modeling and Predicting Complex Systems

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@Article{zhang:1997:entmpcs,
  author =       "Byoung-Tak Zhang and Peter Ohm and Heinz Muehlenbein",
  title =        "Evolutionary Neural Trees for Modeling and Predicting
                 Complex Systems",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "1997",
  volume =       "10",
  number =       "5",
  pages =        "473--483",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 algorithms, neurocomputing, evolutionary neural trees,
                 machine learning, system identification, complex
                 systems, time series prediction",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/S0952-1976(96)00018-8",
  size =         "11 pages",
  abstract =     "Modelling and predicting the behaviour of many
                 technical systems is complicated because they are
                 generally characterised by a large number of variables,
                 parameters and interactions, and limited amounts of
                 collected data. This paper investigates an evolutionary
                 method for learning models of such systems. The models
                 thus evolved are based on trees of heterogeneous neural
                 units. The set of different neuron types is defined by
                 the application domain, and the specific type of each
                 unit is determined during the evolutionary learning
                 process. The structure, size, and weights of the neural
                 trees are also adapted by evolution. Since the genetic
                 search used for training does not require error
                 derivatives, a wide range of neural models can be
                 constructed. This generality is contrasted with various
                 existing methods for complex system modeling, which
                 investigate only restricted topological subsets rather
                 than the complete class of architectures. An
                 improvement in the predictive accuracy and parsimony of
                 models is reported, against backpropagation networks
                 and other well-engineered polynomial-based methods for
                 two problems: MacKey-Glass and Lorenz-like chaotic
                 systems. The authors also demonstrate the importance of
                 the selection pressure towards model parsimony for the
                 improvement of prediction accuracy.",
}

Genetic Programming entries for Byoung-Tak Zhang Peter Ohm Heinz Muhlenbein

Citations