Using Genetic Engineering to find modular structures and activation functions for architectures of artificial neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1226, Issue , 1997, Pages 150-161

  Friedrich, Christoph M ^a   Moraga, Claudio ^b  

^a UNIVERSITY OF WITTEN HERDECKE   (Germany)

^b TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION ANALYSIS; CHEMICAL ACTIVATION; COMPUTATION THEORY; GENETIC ENGINEERING; NEURAL NETWORKS; STRUCTURE (COMPOSITION);

ACTIVATION FUNCTIONS; MODULAR STRUCTURES;

NETWORK ARCHITECTURE;

EID: 84947767208     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-62868-1_107     Document Type: Conference Paper

References (11)

1. L. Altenberg. The evolution of evolvability in genetic programming. In K. E. Kinnear, editor, Advances in Genetic Programming. MIT Press, 1994. http://pueo.mhpcc.edu/altenber/PAPERS/Papers2.html.
2. J. Brauke. Evolutionary algorithms for neural network design and training. In Proceedings of the 1st Nordic Workshop on Genetic Algorithms and its Applications, 1995. ftp://ftp.aifb.uni-karlsruhe.de/pub/jbr/Vaasa.ps.
3. S. Fahlmann and C. Lebiere. The cascade-correlation learning architecture. In Advances in Neural Information Systems-P, pages 525-532. Morgan Kaufmann, 1990.
4. C. M. Friedrich and C. Moraga. An evolutionary method to find good building-blocks for architectures of artificial neural networks. In Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU '96), pages 951-956, Granada, Spain, 1996. ftp://archive.cis.ohiost ate.edu/pub/neuroprose/friedrich.ipmu96.ps.Z.
5. F. Gruau. Cellular encoding of genetic neural networks. Technical Report 92-21, Ecole Normale Superieure de Lyon, Institut IMAG, 1992. ftp://lip.enslyon. fr/pub/Rapports/RR/RR92/RR92-21.ps.Z.
6. F. Gruau. Genetic synthesis of modular neural networks. In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 318-325. Morgan Kaufmann, 1993.
7. R. A. Jacobs, M. I. Jordan, and A. G. Barto. Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. Technical Report COINS Technical Report ' 90-27, University of Massachusetts, Amherst, MA, 1990. ftp://archive.cis.ohiostate. edu/pub/neuroprose/jacobs.modular.ps.Z.
8. J. R. Koza. Genetic programming, on the programming of Computers by means of natural selection. MIT Press, 1992.
9. K. J. Lang and M. J. Witbrock. Learning to tell two spirals apart. In Proceedings of the 1988 Connectionist Models Summer School, 1988.
10. D. Michie, D. J. Spiegelhalter, and C. C. Taylor. Machine Learning, neural and statistical classification. Ellis Horwood Ltd., 1994.
11. Lutz Prechelt. PROBEN1--A set of benchmarks and benchmarking rules for neural network training algorithms. Technical Report 21/94, Fakultt für Informatik, Universität Karlsruhe, D-76128 Karlsruhe, Germany, September 1994. ftp: / /ftp.ira.uka.de/pub /papers/techreports/1994/1994-21.ps.Z.