An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm
Hyperparameters and architecture greatly influence the performance of convolutional neural networks (CNNs); therefore, their optimization is important to obtain the desired results. One of the state-of-the-art methods to achieve this is the use of neuroevolution that utilizes a genetic algorithm (GA...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9462900/ |