Evolutionary system for automatically constructing and adapting radial basis function networks

Neurocomputing

Volumn 69, Issue 16-18, 2006, Pages 2268-2283

  Manrique, Daniel ^a   Ríos, Juan ^a   Rodríguez Patón, Alfonso ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Breast cancer diagnosis; Genetic algorithms; Radial basis function networks; Self adaptive intelligent system

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIAGNOSIS; ENCODING (SYMBOLS); GENETIC ALGORITHMS; MATHEMATICAL MORPHOLOGY; ONCOLOGY; RADIAL BASIS FUNCTION NETWORKS;

BREAST CANCER DIAGNOSIS; GENETIC ALGORITHM RADIAL BASIS FUNCTION NETWORKS (GARBFN); SELF-ADAPTIVE INTELLIGENT SYSTEM;

NEURAL NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BREAST CANCER; CANCER DIAGNOSIS; CODING; COMPUTER AIDED DESIGN; COMPUTER ANALYSIS; CONTROLLED STUDY; GENETIC ALGORITHM; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROBLEM SOLVING; RELIABILITY;

EID: 33748437938     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2005.06.018     Document Type: Article

References (27)

1. D. Barrios, Generalized crossover operator for genetic algorithms (Operador de cruce generalizado en algoritmos genéticos), Ph.D. Thesis, Universidad Politécnica de Madrid, Madrid, 1991.
2. Barrios D., Carrascal A., Manrique D., and Ríos J. Optimisation with real-coded genetic algorithms based on mathematical morphology. International Journal of Computer Mathematics 80 3 (2003) 275-293
3. D. Barrios, A. Carrascal, D. Manrique, J. Ríos, ADANNET: automatic design of artificial neural networks by evolutionary techniques, in: Proceedings of the 21st 2001 SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, Brighton, UK, pp. 67-80.
4. Bianchini M., Frasconi P., and Gori M. Learning without local minima in radial basis function networks. IEEE Transactions on Neural Networks 30 3 (1995) 136-144
5. Brown A.D., and Card H.C. Cooperative coevolution of neural representations. International Journal of Neural Systems 10 4 (2000) 311-320
6. Chen S., Wu Y., and Luk L. Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Transactions on Neural Networks 10 5 (1999) 1239-1243
7. Cohen S., and Intrator N. A hybrid projection-based and radial basis function architecture: initial values and global optimization. Pattern Analysis and Applications 5 2 (2002) 113-120
8. J. Crespo, Morphological connected filters and intra-region smoothing for image segmentation, Ph.D. Thesis, Georgia Institute of Technology, Atlanta, 1993.
9. D'alotto L.A., and Giardina C.R. A Unified Signal Algebra Approach to Two-Dimensional Parallel Digital Signal Processing (1998), Marcel Dekker, New York
10. J. Dorado, Cooperative strategies to select automatically training patterns and neural architectures with genetic algorithms, Ph.D. Thesis, University of La Coruña, Spain, 1999.
11. Eshelman L.J., and Schaffer J.D. Real-coded genetic algorithms and interval-schemata. Foundations of Genetic Algorithms 2 (1993) 187-202
12. Haykin S. Neural Networks: A Comprehensive Foundation. second ed. (1999), Prentice-Hall, Englewood Cliffs, NJ
13. Hervas C., Algar J.A., and Silva M. Correction of temperature variations in kinetic-based determinations by use of pruning computational neural networks in conjunction with genetic algorithms. Journal of Chemical Information and Computers Sciences 40 3 (2000) 724-731
14. Y.S. Hwang, S.Y. Bang, An efficient method to construct a radial basis function neural network classifier, Technical Report, Department of Computer Science & Engineering, Pohang University of Science and Technology, Korea, 1997.
15. Kitano H. Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4 (1990) 461-476
16. Konar A. Artificial Intelligence and Soft Computing (2000), CRC Press, Boca Raton, FL
17. Luo J., Zhou C., and Leung Y. A Knowledge-Integrated RBF Network for Remote Sensing Classification (2001), LREIS, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijin, China
18. Mashor M.Y. Hybrid training algorithms for RBF network. International Journal of the Computer 8 2 (2000) 50-65
19. Michalewicz Z. Genetic Algorithms+Data Structures=Evolution Programs (1999), Springer, New York
20. Musavi M.T., Ahmed W., Chan K.F., Faris K.B., and Hummels D.M. On the training of radial basis function classifiers. Neural Networks 5 (1992) 595-603
21. I. Ono, S. Kobayashi, A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover, in: Proceedings of the 1998 Seventh International Conference on Genetic Algorithms, East Lansing, MI, USA, pp. 246-253.
22. Principe J.C., Euliano N.R., and Lefebvre W.C. Neural and Adaptative Systems, Fundamentals Through Simulations (2000), Wiley, New York
23. N. J. Radcliffe, Genetic neural networks on MIMD computers, Ph.D. Thesis, University of Edinburgh, Edinburgh, UK, 1990.
24. Robbins G.E., Plumbley M.D., Hughes J.C., Fallside F., and Pager R. Generation and adaptation of neural networks by evolutionary techniques (GANNET). Neural Computing and Applications 1 (1993) 23-31
25. F. Sahin, A radial basis function approach to a color image classification problem in a real time industrial application, Ph.D. Thesis, Polytechnic Institute of Virginia, 1997.
26. A.P. Topchy, O.A. Lebedko, V.V. Miagkikh, N.K. Kasabov, Adaptive training of radial basis function networks based on cooperative evolution and evolutionary programming, in: 1998 Progress in Connectionist-Based Information Systems, Springer, Berlin.
27. R. Yampolskiy, D. Novikov, L. Reznik, Experimental study of the neural networks for character recognition, Technical Report, Department of Computer Science, Rochester Institute of Technology, Rochester, New York, 2003.