Evolving Neural Trees for Time Series Prediction Using Bayesian Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Zhang:2000:ECNN,
  author =       "Byoung-Tak Zhang and Dong-Yeon Cho",
  title =        "Evolving Neural Trees for Time Series Prediction Using
                 {Bayesian} Evolutionary Algorithms",
  booktitle =    "Proceedings of the First IEEE Symposium on
                 Combinations of Evolutionary Computation and Neural
                 Networks",
  year =         "2000",
  editor =       "Xin Yao and David B. Fogel",
  pages =        "17--23",
  month =        "11-13 " # may,
  address =      "San Antonio, TX, USA",
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 evolutionary algorithms, evolutionary algorithms,
                 evolutionary computation, neural trees, probabilistic
                 model, small-step mutation-oriented variations, subtree
                 crossover, subtree mutations, time series prediction,
                 tree-structured neural networks, Bayes methods,
                 evolutionary computation, forecasting theory, neural
                 nets, time series, trees (mathematics)",
  DOI =          "doi:10.1109/ECNN.2000.886214",
  ISBN =         "0-7803-6572-0",
  abstract =     "Bayesian evolutionary algorithms (BEAs) are a
                 probabilistic model for evolutionary computation.
                 Instead of simply generating new populations as in
                 conventional evolutionary algorithms, the BEAs attempt
                 to explicitly estimate the posterior distribution of
                 the individuals from their prior probability and
                 likelihood, and then sample offspring from the
                 distribution. We apply the Bayesian evolutionary
                 algorithms to evolving neural trees, i.e.
                 tree-structured neural networks. Explicit formulae for
                 specifying the distributions on the model space are
                 provided in the context of neural trees. The
                 effectiveness and robustness of the method is
                 demonstrated on the time series prediction problem. We
                 also study the effect of the population size and the
                 amount of information exchanged by subtree crossover
                 and subtree mutations. Experimental results show that
                 small-step mutation-oriented variations are most
                 effective when the population size is small, while
                 large-step recombinative variations are more effective
                 for large population sizes.",
}

Genetic Programming entries for Byoung-Tak Zhang Dong-Yeon Cho

Citations