Function approximations by coupling neural networks
and genetic programming trees with oblique decision
trees
Artificial Intelligence in Engineering, 13 (3):
223--239 (1999)DOI: doi:10.1016/S0020-0255(99)00121-8
Abstract
This paper is concerning the development of multiple
neural networks system combined with genetic
programming (GP) trees for problem domains where the
complete input space can be decomposed into several
different regions, and these are well represented in
form of oblique decision tree. The overall architecture
of hybrid system, called the federated agents, consists
of a facilitator, local agents, and boundary agents.
Neural networks used as local agents, each of which is
expert at different subregions, and GP trees serve as
boundary agents. A boundary agent refer to the one that
is specialized at only the borders of subregions where
discontinuities or different patterns may exist. The
facilitator is responsible for choosing the local agent
that is suitable for the given input data using
information obtained from oblique decision tree
representing a divided input space. However, there are
large possibility of selecting the invalid local agent
due to the incorrect prediction of decision tree,
provided that input data is close enough to the
boundaries of regions. Such a situation can lead
federated agents to produce a much higher prediction
error than that of a single neural network trained over
all input space. To deal with this, the approach taken
in this paper is to make the facilitator select the
boundary agent instead of the local agent when input
data is closely located to the certain border of
regions. In this way, even if the result of decision
tree may be incorrect, the results of system are less
affected by it. The validity of our approach is
examined and verified by applying the federated agents
to the configuration design of a midship section of
bulk cargo ships.
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