DocumentCode
518497
Title
Notice of Retraction
The Neural Networks Ensembles based on evolutionary programming for Job Shop Schedule Problem
Author
Liu Fang
Author_Institution
Sch. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
Volume
1
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
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.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
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; neural nets; NNS; evolutionary programming; job shop schedule problem; negative correlation learning; neural networks; Algorithm design and analysis; Artificial neural networks; Automatic programming; Evolutionary computation; Genetic programming; Humans; Job design; Job shop scheduling; Neural networks; Problem-solving; component; correlation learning; evolutionary programming; neural networks ensembles;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5486318
Filename
5486318
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