Abstract
This chapter applies linear GP to the evolution of cooperative teams to several prediction problems. Different linear methods for combining outputs of the team programs are compared. These include hybrid approaches where [1] a neural network is used to optimize the weights of programs in a team for a common decision and [2] a real-numbered vector (the representation of evolution strategies) of weights is evolved in tandem with each team. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method.
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© 2007 Springer Science+Business Media, LLC
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(2007). Evolution of Program Teams. In: Linear Genetic Programming. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31030-5_11
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DOI: https://doi.org/10.1007/978-0-387-31030-5_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-31029-9
Online ISBN: 978-0-387-31030-5
eBook Packages: Computer ScienceComputer Science (R0)