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  Genetic Programming of Minimal Neural Nets Using Occam's Razor (1993) [25 citations — 6 self]

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by Byoung-tak Zhang, Heinz Muhlenbein
Proceedings of the 5th international conference on genetic algorithms (ICGA'93
http://www.geocities.com/francorbusetti/Occamnns.pdf
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Abstract:

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 speci cally de ned genetic operators. The weights are trained by a next-ascent hillclimbing search. A new tness function is proposed that quanti es the principle of Occam's razor. It makes an optimal trade-o between the error tting ability and the parsimony ofthenetwork. We discuss the results for two problems of di ering complexity and study the convergence and scaling properties of the algorithm. 1

Citations

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