文章基本信息

标题：Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution: A Classification Model for Parkinson's Disease
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作者：Sahni, Srishti ; Aggarwal, Vaibhav ; Khanna, Ashish 等
期刊名称：Journal of Information and Organizational Sciences
印刷版ISSN：1846-3312
电子版ISSN：1846-9418
出版年度：2020
卷号：44
期号：2
页码：345-363
DOI：10.31341/jios.44.2.9
出版社：Faculty of Organization and Informatics University of Zagreb
摘要：Parkinson’s Disease is a degenerative neurological disorder with unknown origins, making it impossible to be cured or even diagnosed. The following article presents a Three-Layered Perceptron Neural Network model that is trained using a variety of evolutionary as well as quantum-inspired evolutionary algorithms for the classification of Parkinson's Disease. Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best suited algorithm for Neural Network Weight Distribution. The results show that the quantum-inspired evolutionary algorithms perform better under the given circumstances, with qABC offering the highest accuracy of about 92.3%. The presented model can be used not only for disease diagnosis but is also likely to find its applications in various other fields as well.
关键词：Parkinson’s Disease; Particle Swarm Optimization; Artificial Bee Colony Algorithm; Bat Algorithm; Quantum Optimization; Neural Network;;;