TY - GEN
T1 - A hybrid backpropagation network-based scheduling knowledge acquisition algorithm
AU - Weida, Wang
AU - Wenjian, Liu
PY - 2006
Y1 - 2006
N2 - It is a key issue that constructing a successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. So a hybrid Backpropagation (BP)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search. The hybrid method was utilized to resolve the optimal attributes subset, of manufacturing system and determine the optimal topology and parameters of (BP) under different, scheduling objectives; BP was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal attributes subset, optimal BP's topology and parameters. The experimental results demonstrate that the proposed algorithm produces significant performance improvements over other machine learning-based algorithms.
AB - It is a key issue that constructing a successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. So a hybrid Backpropagation (BP)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search. The hybrid method was utilized to resolve the optimal attributes subset, of manufacturing system and determine the optimal topology and parameters of (BP) under different, scheduling objectives; BP was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal attributes subset, optimal BP's topology and parameters. The experimental results demonstrate that the proposed algorithm produces significant performance improvements over other machine learning-based algorithms.
UR - https://www.scopus.com/pages/publications/38549128180
U2 - 10.1109/ICCIAS.2006.294110
DO - 10.1109/ICCIAS.2006.294110
M3 - Conference contribution
AN - SCOPUS:38549128180
SN - 1424406056
SN - 9781424406050
T3 - 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
SP - 151
EP - 154
BT - 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
PB - IEEE Computer Society
T2 - 2006 International Conference on Computational Intelligence and Security, CIS 2006
Y2 - 3 November 2006 through 6 November 2006
ER -