Evolutionary system for automatically constructing and adapting radial basis function networks

Abstract This article presents a new system for automatically constructing and training radial basis function networks based on original evolutionary computing methods. This system, called Genetic Algorithm Radial Basis Function Networks (GARBFN), is based on two cooperating genetic algorithms. The first algorithm uses a new binary coding, called basic architecture coding, to get the neural architecture that best solves the problem. The second, which uses real coding, takes its inspiration from mathematical morphology theory and trains the architectures output by the binary genetic algorithm. This system has been applied to a laboratory problem and to breast cancer diagnosis. The results of these evaluations show that the overall performance of GARBFN is better than other related approaches, whether or not they are based on evolutionary techniques.

[1]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 2000, Springer Berlin Heidelberg.

[2]  Louis D'Alotto,et al.  A Unified Signal Algebra Approach to Two-Dimensional Parallel Digital Signal Processing , 1998 .

[3]  Nikola K. Kasabov,et al.  Adaptive Training of Radial Basis Function Networks Based on Cooperative Evolution and Evolutionary Programming , 1997, ICONIP.

[4]  Mohamad T. Musavi,et al.  On the training of radial basis function classifiers , 1992, Neural Networks.

[5]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[6]  César Hervás-Martínez,et al.  Correction of Temperature Variations in Kinetic-Based Determinations by Use of Pruning Computational Neural Networks in Conjunction with Genetic Algorithms , 2000, J. Chem. Inf. Comput. Sci..

[7]  Nathan Intrator,et al.  A Hybrid Projection Based and Radial Basis Function Architecture , 2000, Multiple Classifier Systems.

[8]  Howard C. Card,et al.  Cooperative Coevolution of Neural Representations , 2000, Int. J. Neural Syst..

[9]  Sheng Chen,et al.  Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks , 1999, IEEE Trans. Neural Networks.

[10]  Sung Yang Bang,et al.  An Efficient Method to Construct a Radial Basis Function Neural Network Classifier , 1997, Neural Networks.

[11]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[12]  Shigenobu Kobayashi,et al.  A Real-Coded Genetic Algorithm for Function Optimization Using the Unimodal Normal Distribution Crossover , 1999 .

[13]  V. Kvasnicka,et al.  Neural and Adaptive Systems: Fundamentals Through Simulations , 2001, IEEE Trans. Neural Networks.

[14]  Ferat Sahin.  A Radial Basis Function Approach to a Color Image Classification Problem in a Real Time Industrial Application , 1997 .

[15]  Mark D. Plumbley,et al.  Generation and adaptation of neural networks by evolutionary techniques (GANNET) , 2005, Neural Computing & Applications.

[16]  J. Crespo.  Morphological connected filters and intra-region smoothing for image segmentation , 1993 .

[17]  Hiroaki Kitano,et al.  Designing Neural Networks Using Genetic Algorithms with Graph Generation System , 1990, Complex Syst..

[18]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[19]  Daniel Manrique,et al.  Optimisation With Real-Coded Genetic Algorithms Based On Mathematical Morphology , 2003, Int. J. Comput. Math..

[20]  Daniel Manrique,et al.  ADANNET: Automatic Design of Artificial Neural Networks by Evolutionary Techniques , 2002 .

[21]  Jiancheng Luo,et al.  A KNOWLEDGE-INTEGRATED RBF NETWORK FOR REMOTE SENSING CLASSIFICATION , 2001 .

[22]  Dolores Barrios Rolanía.  Operador de cruce generalizado en algoritmos genéticos , 1992 .

[23]  Isao Ono,et al.  A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover , 1997, ICGA.

[24]  Nicholas J. Radcliffe,et al.  Genetic neural networks on MIMD computers , 1992 .

[25]  Paolo Frasconi,et al.  Learning without local minima in radial basis function networks , 1995, IEEE Trans. Neural Networks.

[26]  M. Y. Mashor.  Hybrid training algorithm for RBF Network , 2000 .