An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks

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

@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",
• broken = "ftp://archive.cis.ohio-state.edu/pub/neuroprose/friedrich.ipmu96.ps.Z",
• URL = "http://citeseer.ist.psu.edu/friedrich96evolutionary.html",
• 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.",

}

Genetic Programming entries for Christoph M Friedrich Claudio Moraga

Citations