Zusammenfassung
A genetic programming method is investigated for
optimizing both the architecture and the connection
weights of multilayer feedforward neural networks. The
genotype of each network is represented as a tree whose
depth and width are dynamically adapted to the
particular application by specifically defined genetic
operators. The weights are trained by a next-ascent
hillclimbing search. A new fitness function is proposed
that quantifies the principle of Occam's razor. It
makes an optimal trade-off between the error fitting
ability and the parsimony of the network. We discuss
the results for two problems of differing complexity
and study the convergence and scaling properties of the
algorithm.
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