An evolutionary algorithm for designing feedforward neural networks

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

Volumn 1447, Issue , 1998, Pages 629-635

  Skourikhine, Alexei N ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING; GENETIC ALGORITHMS; MULTILAYER NEURAL NETWORKS; SINGULAR VALUE DECOMPOSITION; TOPOLOGY;

EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS; EVOLVING NEURAL NETWORK; INPUT VARIABLES; MESSY GENETIC ALGORITHMS; NEW APPROACHES; SINGULAR VALUES; VARIABLE LENGTH; VARIABLE NUMBER;

FEEDFORWARD NEURAL NETWORKS;

EID: 84947804167     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0040814     Document Type: Conference Paper

References (14)

1. Schaffer, J.D., Whitley, D., and Eshelman, L.J., "Combinations of genetic algorithms and neural networks: A survey of the state of the art," Combinations of Genetic Algorithms and Neural Networks (Whitley, L.D., Schaffer, J.D., eds.), 1-37, Los Alamitos, CA: IEEE Computer Society Press, June 1992.
2. Yao, X., "A review of evolutionary artificial neural networks," International Journal of Intelligent Systems, 8(4), 539-567, 1993.
3. Smith, R., Cribbs III, H.B., "Is a learning classifier system a type of neural network?," Evolutionary Computation, 2(1), 19-36, 1994.
4. Smalz, R., Conrad, M., "Combining evolution with credit apportionment: a new learning algorithm for neural nets," Neural Networks, 7(2), 341-351, 1994.
5. Finnoff, W., Hergert, F., Zimmermann, H.G., "Improving model selection by nonconvergent methods," Neural Networks, 6, 771-783.
6. Fahlman, S.E. and Lebiere, C., "The cascade-correlation learning architecture," Advances in Neural Information Processing Systems, 2, (Lippmann, R.P., Moody, J.E., Touretzky, D.S., eds.), 524-532, San Francisco: Morgan Kaufmann, 1991.
7. Goldberg, D., Deb, K., and Korb, B., "Messy genetic algorithms: Motivation, analysis, and first results," Complex Systems, 3, 493-530, 1989.
8. Goldberg, D., Deb, K., and Korb, B., "Messy genetic algorithms revisited: Studies in mixed size and scale," Complex Systems, 4, 415-444, 1990.
9. Forsythe, G.E., Malcolm, M.A., and Moler, C.B., Computer methods for mathematical computations, Prentice-Hall, Inc., 1977.
10. Fahlman, S.E., "An empirical study of learning speed in back-propagation networks," Carnegie Mellon University Technical Report CMU-CS-88-162, September 1988.
11. Rumelhart, D.E., Hinton, G.E., and Williams, R.J., "Learning internal representations by error propagation," In: Parallel Distributed Processing, MIT Press, 1, 318-362, 1986.
12. Goldberg, D.E., Genetic algorithms in search optimization and machine learning, Reading, MA, Addison-Wesley, 1989.
13. Back, T., Hoffmeister, F., Schwefel H.-P., "A survey of evolutionary strategies," Proceedings of the 4th International Conference on Genetic Algorithms and their Applications, edited by Belew R.K. and Booker L.B., pp. 2-9, 1991, Morgan Kaufmann, San Mateo, CA.
14. Gorman, R.P., and Sejnowski, T.J., "Analysis of hidden units in a layered network trained to classify sonar targets," Neural Networks, 1, 75-89, 1988.