Abstract
Deep learning models are immensely used for numerous problems in different domains and have proven to be superior over feature-based machine learning techniques. However, the success of any deep learning model is dependent on several factors like the tuning of appropriate different hyper-parameters, neural network architectures, optimizers, etc. To learn neural network weights, gradient-based optimizers such as stochastic gradient descent, min-batch gradient descent and the Adam optimizer are widely used. However, the architecture of neural networks and hyper-parameters has to be tuned manually for better performance of the model. Evolutionary deep learning models are gaining attention in recent years to overcome the manual tuning of hyper-parameters and the network architecture. This paper presents a design perspective of recently developed Genetic Algorithm-driven evolutionary deep learning models along with the inherent challenges.
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Acknowledgements
This work is supported by the Science and Engineering Board (SERB), Department of Science and Technology (DST) of the Government of India under Grant No. ECR/2018/000204 and Grant No. EEQ/2019/000657.
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Sarkar, D., Mishra, N., Biswas, A. (2022). Genetic Algorithm-Based Deep Learning Models: A Design Perspective. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_27
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DOI: https://doi.org/10.1007/978-981-16-6890-6_27
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