Title :
Neural Network Optimized by Improved Genetic Algorithms to Diagnosis
Author :
Liu, Lijuan ; Yi, Xiaomei
Author_Institution :
Dept. of Inf. Eng., Zhejiang A& F Univ., Hangzhou, China
Abstract :
In this study, diagnosis was performed with an evolutionary artificial neural network approach based on adaptive genetic algorithm with strong macro-search capability and global optimization, which was used to optimize initial weights and thresholds of the network. Simulation showed that the algorithm could be used for computer aided diagnosis after a large number of liver abscess cases had learned.
Keywords :
evolutionary computation; genetic algorithms; neural nets; adaptive genetic algorithm; computer aided diagnosis; evolutionary artificial neural network; global optimization; macro search capability; neural network optimisation; Adaptive systems; Artificial neural networks; Biological neural networks; Genetic algorithms; Genetics; Liver; adaptive genetic algorithm; diagnosis; evolutionary artificial neural network; weights and thresholds;
Conference_Titel :
Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4673-1450-3
DOI :
10.1109/ICICEE.2012.314
