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Despite recent innovations in network architectures and loss functions,training RNNs to learn long-term dependencies remains difficult due tochallenges with gradient-based optimisation methods. Inspired by the success ofDeep Neuroevolution in reinforcement learning (Such et al. 2017), we explorethe use of gradient-free population-based global optimisation (PBO) techniques-- training RNNs to capture long-term dependencies in time-series data. Testingevolution strategies (ES) and particle swarm optimisation (PSO) on anapplication in volatility forecasting, we demonstrate that PBO methods lead toperformance improvements in general, with ES exhibiting the most consistentresults across a variety of architectures.

Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs