Structure Design of Neural Networks Using Genetic Algorithms

A method for designing and training neural networks using genetic algorithms is proposed, with the aim of getting the optimal structure of the network and the optimized parameter set simultaneously. For this purpose, a fitness function depending on both the output errors and simpleness in the structure of the network is introduced. The validity of this method is checked by experiments on four logical operation problems: XOR, 6XOR, 4XOR-2AND, and 2XOR-2AND-2OR; and on two other problems: 4-bit pattern copying and an 8 ! 8-encoder/decoder. It is concluded that, although this method is less powerful for disconnected networks, it is useful for connected ones.

[1]  Sankar K. Pal,et al.  Selection of Optimal Set of Weights in a Layered Network Using Genetic Algorithms , 1994, Inf. Sci..

[2]  Swapan Saha,et al.  Genetic Design of Sparse Feedforward Neural Networks , 1994, Inf. Sci..

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[5]  D. R. McGregor,et al.  Designing application-specific neural networks using the structured genetic algorithm , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[6]  Oded Maimon,et al.  A Distributed Genetic Algorithm for Neural Network Design and Training , 1992, Complex Syst..

[7]  Mike Mannion,et al.  Complex systems , 1997, Proceedings International Conference and Workshop on Engineering of Computer-Based Systems.

[8]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[9]  Stefan Bornholdt,et al.  General asymmetric neural networks and structure design by genetic algorithms: a learning rule for temporal patterns , 1992, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.