Genetic Programming of Minimal Neural Nets Using Occam's Razor

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{icga93:zhang,

• author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
• title = "Genetic Programming of Minimal Neural Nets Using {O}ccam's Razor",
• year = "1993",
• booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93",
• publisher = "Morgan Kaufmann",
• editor = "Stephanie Forrest",
• pages = "342--349",
• address = "University of Illinois at Urbana-Champaign",
• month = "17-21 " # jul,
• URL = "http://www.muehlenbein.org/gpnn93.pdf",
• URL = "http://www.ais.fraunhofer.de/~muehlen/publications/gmd_as_ga-93_04.ps",
• keywords = "genetic algorithms, genetic programming",
• size = "8 pages",
• 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 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.",
• notes = "GP feedforward binary ANN",

}

Genetic Programming entries for Byoung-Tak Zhang Heinz Muhlenbein

Citations