论文信息 - A Genetic Algorithm Optimized New Structured Neural Network for Multistage Decision-Making Problem

A Genetic Algorithm Optimized New Structured Neural Network for Multistage Decision-Making Problem

For the widely use of multistage decision-making problem in our normal life such as in the new research area of dynamic selection of composite web services, this paper exerts all its effort on proposing a new approach to solve such problem. Motivated by neural networks’ high parallel performance and Genetic Algorithm’s powerful computation, a novel Genetic Algorithm optimized neural network is proposed in this paper for this task. In order to make this algorithm more adaptable for multistage decision-making problem, a new neural network structure for implementing the algorithm is proposed which is a modification to the one used by Thomopoulos or Rauch and Winarske.

Yan Gao | Bin Zhang | Lei Yang | Yu Dai

[1] S.C.A. Thomopoulos,et al. Neural network implementation of the shortest path algorithm for traffic routing in communication networks , 1989, International 1989 Joint Conference on Neural Networks.

[2] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[3] Pingzhi Fan,et al. Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies , 2004 .

[4] J. J. Hopfield,et al. “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[5] H.E. Rauch,et al. Neural networks for routing communication traffic , 1988, IEEE Control Systems Magazine.

[6] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7] Bernardete Ribeiro,et al. A neural network for shortest path computation , 2001, IEEE Trans. Neural Networks.

[8] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.