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@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