Created by W.Langdon from gp-bibliography.bib Revision:1.7182
This dissertation addresses the problem of forecast model identification by extending the use of cognitive science techniques into a framework for the natural selection of Bayesian networks. This framework consists of a network model that has two components. The first component is a genetic programming based representation that decomposes into two parts; decision making and information gathering. The decision making part is represented as a Modified Naive Bayesian classifier. The information gathering part is represented as an attribute generator that classifies input values according to numerical categories. The search process for identifying the network components is done using natural selection as implemented using genetic programming. The second framework component uses the resultant Bayesian model to forecast future values of a given time series by using the maximum a posteriori (MAP) hypothesis generated by querying the Bayesian network.
Using the framework for naturally selecting Bayesian networks, experimental results are presented for both synthesized time series and data sets drawn from the daily values of individual stocks traded on the NASDAQ and NYSE. Using a Random Walk as the benchmark forecast, results from three variations of the naturally selected Bayesian network forecaster with single variable inputs are compared to results obtained using a neural network. Additional information detailing network structure and forecasting accuracy is presented for the best models describing each time series forecasted.",
Genetic Programming entries for Andrew J Novobilski