Deep Learning through Evolution: A Hybrid Approach to Scheduling in a Dynamic Environment
Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However the method is not without its 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 on the other hand can be trained at length off-line, before being deployed on-line, allowing for fast generation of solutions. 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 optimized human-competitive solutions. Deep Learning is then deployed to extract the underlying model within the GA, enabling fast optimisation on unseen data.