Evolutionary strategy for classification problems and its application in fault diagnostics |
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| Affiliation: | 1. University of Perugia, Italy;2. University of Rome Tor Vergata, Italy;3. EIEF, Italy;1. Department of Linguistics, University of Potsdam, Potsdam, Germany;2. Department of Statistics, Columbia University, New York, USA;1. School of Mathematics and Statistics, Hubei Minzu University, Enshi, Hubei 445000, China;2. School of Information Science and Technology, Nantong University, Nangtong 226019, China;3. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam;4. Andalusian Research Institute DaSCI Data Science and Computational Intelligence, University of Granada, Spain;5. Faculty of Software and Information Science, Iwate Prefectural University, Sugo Takizawa, Iwate 020-0193, Japan;1. Computer Science Department, Baoji University of Arts and Sciences, Baoji, China;2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan;3. Department of Civil Engineering, Al-Maaref University College, Ramadi, 31001, Iraq;4. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar 64001, Iraq;5. School of Computing and Informatics, Al Hussein Technical University, Amman, Jordan;6. Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia;1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong;2. Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong |
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| Abstract: | Genetic algorithms (GAs) based evolutionary strategy is proposed for classification problems, which includes two aspects: evolutionary selection of the training samples and input features, and evolutionary construction of the neural network classifier. For the first aspect, the GA based k-means-type algorithm (GKMT) is proposed, which combines GA and k-means-type (KMT) to achieve the optimal selection of the training samples and input features simultaneously. By this algorithm, the “singular” samples will be eliminated according to the classification accuracy and the features that facilitate the classification will be enhanced. On the opposite, the useless features will be suppressed and even eliminated. For the second aspect, the hierarchical evolutionary strategy is proposed for the construction and training of the neural network classifier (HENN). This strategy uses the hierarchical chromosome to encode the structure and parameters of the neural network into control genes and parameter genes respectively, designs and trains the network simultaneously. Finally, the experimental study pertained to the fault diagnostics for the rotor-bearing system is given and the results presented show that the proposed evolutionary strategy for the classification problem is feasible and effective. |
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