- author = "David Fagan and Michael Fenton and David Lynch and Stepan Kucera and Holger Claussen and Michael O'Neill",
- title = "Deep learning through evolution: A hybrid approach to scheduling in a dynamic environment",
- booktitle = "2017 International Joint Conference on Neural Networks (IJCNN)",
- year = "2017",
- pages = "775--782",
- month = may,
- publisher = "IEEE Press",
- email = "david.fagan@ucd.ie",
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Bandwidth, ANN, Computer architecture, Downlink, Interference, Schedules, Signal to noise ratio",
-
DOI = "
doi:10.1109/IJCNN.2017.7965930",
- size = "8 pages",
- abstract = "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.",
-
notes = "Not GP? Comparison with GE?
also known as \cite{7965930}",
