Island Transpeciation: A Co-Evolutionary Neural Architecture Search, Applied to Country-Scale Air-Quality Forecasting

Abstract

Air pollution causes around 400 000 premature deaths per year in Europe due to Particulate Matter, nitrogen oxides, and ground-level ozone pollutants. Multiple-input multiple-output nonlinear auto-regressive exogenous deep neural networks are frequently used to predict a day before, air-quality pollution incidents, at a country scale. With complexity and data sizes increasing, finding performant models becomes harder. We propose island transpeciation to optimize hyperparameters and architectures. Unlike using a single optimizer, island transpeciation combines results from multiple optimizers, to consistently provide excellent performance. Moreover, we show that island transpeciation outperforms random model search and other previous modeling efforts. Island transpeciation is a neural architecture search that uses co-evolution (genes), to combine (transpeciation) populations of incompatible optimizers (species) organized in island formations. In island transpeciation, architecture search is parallelized and utilizes a distributed pool of hardware resources. We have successfully used these techniques to predict next-day ozone concentrations across the Belgian territory.