Abstract
An evolutionary approach for developing improved neural network architectures is presented. It is shown that it is possible to use genetic algorithms for the construction of backpropagation networks for real world tasks. Therefore a network representation is developed with certain properties. Results with various application are presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
D. Fox, V. Heinze, K. Möller, S. Thrun, and G. Veenker. Learning by error-driven decomposition. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, volume 1, pages 207–212, Amsterdam, June 1991. North-Holland.
D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
S. A. Harp, T. Samad, and A. Guha. Designing application-specific neural networks using the genetic algorithm. In D. S. Touretzky, editor, Proceedings of IEEE conference on Neural Information Processing Systems, volume 2, pages 447–454, San Mateo, 1990. (Denver 1989), Morgan Kaufmann.
J. S. Judd. Neural Network Design and the Complexity of Learning. MIT Press — Bradford Book, Cambridge, MA, 1990.
H. J. Lin and J. S. Vitter. Complexity issues in learning by neural networks. Technical Report TR-CS-90-01, Department of Computer Science, Brown University, Providence, II, 1990.
M. Mandischer. Genetische Algorithmen zur Optimierung Konnektionistischer Modelle. Master’s thesis, Department of Computer Science, University of Dortmund, Dortmund, PO BOX 500500, February 1992.
G. F. Miller, P. M. Todd, and S. U. Hegde. Designing neural networks using genetic algorithms. In J. D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 379-384, San Mateo, 1989. (Arlington 1989), Morgan Kaufmann.
T. Rohne. Künstliche neuronale netze zur eckendetektion in digitalisierten grauwertbildern. Master’s thesis, University of Dortmund, Department of Computer Science X, Dortmund, FRG, 1991.
N. Schaudolph and R. K. Belew. Dynamic parameter encoding for genetic algorithms. Technical Report CSE-TR#CS90-175, University of California, San Diego, CA, 1990.
W. Schiffmann, J. Merten, and W. Randolf. Performance evaluation of evolutionary created neural network topologies. In First International Workshop on Parallel Problem Solving from Nature, pages A-III:-1-11, Dortmund, FRG, 1990. University of Dortmund, Department of Computer Science X.
A. Ultsch, R. Hannuschka, U. Hartmann, M. Mandischer, and V. Weber. Optimizing symbolic proofs with connectionist models. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, volume 1, pages 585–590, Amsterdam, June 1991. North-Holland.
G. Weiß. Combining neural and evolutionary learning: Aspects and approaches. Technical Report FKI-132-90, Technische Universität München, 1990.
H. White and K. Hornik. Universal approximation using feedforward networks with nonsigmoid hidden layer activation functions. In Proceedings of the International Joint Conference on Neural Networks. (San Diego, CA), IEEE, 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag/Wien
About this paper
Cite this paper
Mandischer, M. (1993). Representation and Evolution of Neural Networks. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_93
Download citation
DOI: https://doi.org/10.1007/978-3-7091-7533-0_93
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82459-7
Online ISBN: 978-3-7091-7533-0
eBook Packages: Springer Book Archive