TY - CHAP
T1 - Deep learning through evolution
T2 - 2017 International Joint Conference on Neural Networks, IJCNN 2017
AU - Fagan, David
AU - Fenton, Michael
AU - Lynch, David
AU - Kucera, Stepan
AU - Claussen, Holger
AU - O'Neill, Michael
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks. GAs require time to run, and evolve a bespoke solution to the desired problem in real time. This requirement can prove to be prohibitive in a high-frequency dynamic environment where on-line training time is limited. Neural Networks (NNs) on the other hand can be trained at length off-line, before being deployed on-line, allowing for fast generation of solutions on demand. This study presents a hybrid approach to time-frame scheduling in a high frequency domain. A GA approach is used to generate a dataset of optimised human-competitive solutions. Deep Learning is then deployed to extract the underlying model within the GA, enabling fast optimisation on unseen data. This hybrid approach allows for NNs to generate GA-quality schedules on-line, almost 100 times faster than running the GA.
AB - Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks. GAs require time to run, and evolve a bespoke solution to the desired problem in real time. This requirement can prove to be prohibitive in a high-frequency dynamic environment where on-line training time is limited. Neural Networks (NNs) on the other hand can be trained at length off-line, before being deployed on-line, allowing for fast generation of solutions on demand. This study presents a hybrid approach to time-frame scheduling in a high frequency domain. A GA approach is used to generate a dataset of optimised human-competitive solutions. Deep Learning is then deployed to extract the underlying model within the GA, enabling fast optimisation on unseen data. This hybrid approach allows for NNs to generate GA-quality schedules on-line, almost 100 times faster than running the GA.
UR - https://www.scopus.com/pages/publications/85031048980
U2 - 10.1109/IJCNN.2017.7965930
DO - 10.1109/IJCNN.2017.7965930
M3 - Chapter
AN - SCOPUS:85031048980
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 775
EP - 782
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 May 2017 through 19 May 2017
ER -