Optimising Evolution of SA-UNet for Iris Segmentation

Neuroevolution is the process of building or enhancing neural networks through the use of an evolutionary algorithm. An improved model can be defined as improving a model’s accuracy or finding a smaller model with faster training time with acceptable performance. Neural network hyper-parameter tuning is costly and timeconsuming and often expert knowledge is required. In this study we investigate various methods to increase the performance of evolution, namely, epoch early stopping, using both improvement and threshold validation accuracy to stop training bad models, and removing duplicate models during the evolutionary process. Our results demonstrated the creation of a smaller model, 7:3M, with higher accuracy, 0:969, in comparison to previously published methods. We also benefit from an average time saving of 59% because of epoch optimisation and 51% from the removal of duplicated individuals, compared to our prior work.