|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|105823||2018||8 صفحه PDF||سفارش دهید||3613 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia Computer Science, Volume 125, 2018, Pages 525-532
In recent years, deep learning has been extensively used in both supervised and unsupervised learning problems. Among the deep learning models, CNN has outperformed all others for object recognition task. Although CNN achieves exceptional accuracy, still a huge number of iterations and chances of getting stuck in local optima makes it computationally expensive to train. Genetic Algorithm is a metaheuristic approach inspired by the theory of natural selection and has been used for solving both bounded and unbounded optimization problems by a large success. To handle these issues, we have developed a hybrid deep learning model using Genetic Algorithm and L-BFGS method for training CNN. To test our model, we have taken the Devanagari handwritten numeral dataset. Our results show that GA assisted CNN produces better results than non-GA assisted CNN. This study concludes that evolutionary technique can be used to train CNN more efficiently.