Title :
Parallel back-propagation using genetic algorithm: real-time BP learning on the massively parallel computer CP-PACS
Author :
Yasunaga, Moritoshi ; Yoshida, Eiji ; Yoshihara, Ikuo
Author_Institution :
Inst. of Inf. Sci. & Electron., Tsukuba Univ., Ibaraki, Japan
Abstract :
In this paper, we propose a parallel backpropagation algorithm using the genetic algorithm to reduce the learning time to reach the optimal solution. Genetic algorithm is used in parallel previous to the parallel backpropagation regarding the set of weights in the feedforward neural network as chromosomes, and well-evolved chromosomes (sets of excellent initial weights) are used in the parallel backpropagation. Performance of the proposed algorithm was evaluated experimentally in 5-bit and 8-bit parity problems using the massively parallel computer CP-PACS. The learning speeds were about 3 times and about 7 times faster than those of the simple parallel backpropagation in the 5-bit and the 8-bit parity problems, respectively
Keywords :
backpropagation; computational complexity; feedforward neural nets; genetic algorithms; parallel processing; real-time systems; 5-bit parity problems; 8-bit parity problems; GA; feedforward neural network; genetic algorithm; massively parallel computer CP-PACS; parallel back-propagation; parallel backpropagation algorithm; real-time BP learning; well-evolved chromosomes; Artificial neural networks; Biological cells; Chromosome mapping; Computer networks; Concurrent computing; Genetic algorithms; Iterative algorithms; Large-scale systems; Neural networks; Real time systems;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830834
