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

by

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.
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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.",
}