Optimising Evolution of SA-UNet for Iris Segmentation

Mahsa Mahdinejad, Aidan Murphy, Patrick Healy, Conor Ryan

2023

Abstract

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 time-consuming 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.

Paper Citation

in Harvard Style

Mahdinejad M., Murphy A., Healy P. and Ryan C. (2023). Optimising Evolution of SA-UNet for Iris Segmentation. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 901-908. DOI: 10.5220/0011798600003393

in Bibtex Style

@conference{icaart23,
author={Mahsa Mahdinejad and Aidan Murphy and Patrick Healy and Conor Ryan},
title={Optimising Evolution of SA-UNet for Iris Segmentation},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={901-908},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011798600003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Optimising Evolution of SA-UNet for Iris Segmentation
SN - 978-989-758-623-1
AU - Mahdinejad M.
AU - Murphy A.
AU - Healy P.
AU - Ryan C.
PY - 2023
SP - 901
EP - 908
DO - 10.5220/0011798600003393