# An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks   [ANN] [NN]

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## Friedrich, C., M. and Moraga, C.

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 Info: Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU '96) (Conference proceedings), 1996, p. 951-956 Keywords: genetic algorithms, genetic programming Abstract: This paper deals with the combination of Evolutionary Algorithms [EA] and Artificial Neural Networks (ANN). [ANN] [NN] A new method is presented, to find good building-blocks for architectures of Artificial Neural Networks. [ANN] [NN] The method is based on \em Cellular Encoding, [CE] a representation scheme by F. Gruau, and on Genetic Programming [GP] by J. Koza. First it will be shown that a modified Cellular Encoding technique [CE] is able to find good architectures even for non-boolean networks. With the help of a graph-database and a new graph-rewriting method, it is secondly possible to build architectures from modular structures. The information about building-blocks for architectures is obtained by statistically analyzing the data in the graph-database. Simulation results for two real-world problems are given. URL(s): (G)zipped postscript Review item: Mark as doublet (will be reviewed)

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BibTex:
 @InProceedings{friedrich:1996:emfgbb, author = "Christoph M. Friedrich and Claudio Moraga", title = "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks", booktitle = "Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU '96)", year = "1996", pages = "951--956", address = "Granada, Spain", keywords = "genetic algorithms, genetic programming", URL = "ftp://archive.cis.ohio-state.edu/pub/neuroprose/friedrich.ipmu96.ps.Z", abstract = "This paper deals with the combination of Evolutionary Algorithms and Artificial Neural Networks (ANN). A new method is presented, to find good building-blocks for architectures of Artificial Neural Networks. The method is based on {\em Cellular Encoding}, a representation scheme by F. Gruau, and on Genetic Programming by J. Koza. First it will be shown that a modified Cellular Encoding technique is able to find good architectures even for non-boolean networks. With the help of a graph-database and a new graph-rewriting method, it is secondly possible to build architectures from modular structures. The information about building-blocks for architectures is obtained by statistically analyzing the data in the graph-database. Simulation results for two real-world problems are given.", }