Evolutionary Deep Learning

Evolutionary deep learning (EDL) combines evolutionary computation techniques with deep learning to automate the design and optimization of neural network architectures and hyperparameters, overcoming the limitations of manual design and improving efficiency. Current research focuses on developing efficient algorithms, such as those based on genetic programming and policy gradients, to evolve various architectures including convolutional neural networks (CNNs) and spiking neural networks (SNNs), often incorporating transfer learning strategies. This approach offers significant advantages in terms of reduced computational cost, improved accuracy, and enhanced interpretability compared to traditional deep learning methods, impacting fields like image classification and fault diagnosis.