Abstract
Software visualization is an area of computer science devoted to supporting the understanding and effective use of algorithms. The application of software visualization to Evolutionary Computation has been receiving increasing attention during the last few years. In this paper we apply visualization technique to an evolutionary algorithm for multilayer perceptron training. Our goal is to better understand its internal behavior in order to improve the evolutionary part of the method. As a result of applying this this technique several deficiencies in the method have been discovered.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
T.F. Cox and M.A.A. Cox. Multidimensional Scaling. London: Chapman & Hall, 1994.
B.D. Ripley. Pattern Recognition and Neural Networks. Cambridge, GB: Cambridge University Press, 1996.
J.W. Sammon Jr. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, pages 401–409, 1969.
Matlab-User Guide. Natick, Mass: The Mathworks, Inc, 1994–1996.
M. Riedmiller and H. Braun. A direct adapatative method for faster backpropa-gation learning: The RPROP algorithm. In H. Ruspini, editor, Proceedings of the IEEE International Conference on Neural Networks (ICNN), pages 586–591, 1993.
T. Kohonen. The Self-Organizing Map. In Proceedings of the IEEE, volume 78, pages 1464–1480, 1990.
S.E. Fahlman. Faster-Learning Variations on Back-Propagation: An Empirical Study. Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, 1988.
P.A. Castillo; J.J. Merelo; V. Rivas; G. Romero; A. Prieto. G-Prop: Global Optimization of Multilayer Perceptrons using GAs. Submitted to Neurocomputing (2nd revision), 1999.
P.A. Castillo; J. González; J.J. Merelo; V. Rivas; G. Romero; A. Prieto. SA-Prop: Optimization of Multilayer Perceptron Parameters using Simulated Annealing. In Lecture Notes in Computer Science, Volume I, volume 1606, pp. 661–670, 1998.
P.A. Castillo; J. González; J.J. Merelo; V. Rivas; G. Romero; A. Prieto. G-Prop-II: Global Optimization of Multilayer Perceptrons using GAs. In Congress on Evolutionary Computation, Volume III, pp. 2022–2027, Washington D.C., USA, 1999.
P.A. Castillo; J. González; J.J. Merelo; V. Rivas; G. Romero; A. Prieto. G-Prop-III: Global Optimization of Multilayer Perceptrons using an Evolutionary Algorithm. In Congress on Evolutionary Computation, In Genetic and Evolutionary Computation Conference, Volume I, pp. 942, Orlando, USA, 1999.
P.A. Castillo; J. Carpio; J.J. Merelo; V. Rivas; G. Romero; A. Prieto. Evolving Multilayer Perceptrons. To appear in Neural Proccesing Letters, vol. 12, issue 2. October 2000, 1999.
P.A. Castillo; M.G. Arenas; J.G. Castellano; J. Carpio; J.J. Merelo; A. Prieto; V. Rivas; G. Romero. Function approximation with evolved multilayer perceptrons. Submitted to PPSN2000.
O. L. Mangasarian; R. Setiono and W.H. Wolberg. Pattern recognition via linear programming: Theory and application to medical diagnosis. Large-scale numerical optimization, Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22–30, 1990.
Lutz Prechelt. PROBEN1 — A set of benchmarks and benchmarking rules for neural network training algorithms. Technical Report 21/94, Fakultät für Informatik, Universität Karlsruhe, D-76128 Karlsruhe, Germany, September 1994.
C. San Martin; C. Grass; J.M. Carazo. Six molecules of SV40 large t antigen assemble in a propeller-shaped particle around a channel. Journal of Molecular Biology, page in press, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Romero, G. et al. (2000). Evolutionary Computation Visualization: Application to G-PROP. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_88
Download citation
DOI: https://doi.org/10.1007/3-540-45356-3_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41056-0
Online ISBN: 978-3-540-45356-7
eBook Packages: Springer Book Archive