@article{Sahni_Aggarwal_Khanna_Gupta_Bhattacharyya_2020, title={Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution: A Classification Model for Parkinson’s Disease: A Classification Model for Parkinson’s Disease}, volume={44}, url={//jios.foi.hr/index.php/jios/article/view/1373}, DOI={10.31341/jios.44.2.9}, abstractNote={<p><span style="font-weight: 400;">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.</span></p>}, number={2}, journal={Journal of Information and Organizational Sciences}, author={Sahni, Srishti and Aggarwal, Vaibhav and Khanna, Ashish and Gupta, Deepak and Bhattacharyya, Siddhartha}, year={2020}, month={Dec.} }