Deep learning through evolution: A hybrid approach to scheduling in a dynamic environment

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Fagan:2017:IJCNN,
  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}",
}

Genetic Programming entries for David Fagan Michael Fenton David Lynch Stepan Kucera Holger Claussen Michael O'Neill

Citations