Title :
The neural networks ensembles solving job shop schedule problem based on evolutionary programming
Author_Institution :
Sch. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
Abstract :
A evolutionary programming is proposed in this paper to automatically design neural networks(NNS) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to find the best collaboration connection during the evolutionary process. In addition, the architecture of each NN in the ensemble and the size of the ensemble need not to be predefined. The Neural Networks Ensembles based on evolutionary programming is designed in order to solve Job Shop Schedule Problem. The simulation results show that the proposed method in this paper is valid.
Keywords :
evolutionary computation; job shop scheduling; learning (artificial intelligence); neural nets; evolutionary programming; job shop schedule problem; negative correlation learning; neural networks ensemble; Algorithm design and analysis; Artificial neural networks; Correlation; Evolutionary computation; Optimization; Programming; Training; correlation learning; evolutionary programming; neural networks ensemble;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554068
