Discovery of multivariable algebraic expressions using evolutionary optimization

Abstract

Machine learning interpretation has a well-established discussion in various areas. Whereas most interpretation is made after the modelling, one may try to obtain the informative by itself model. Mainly such models are represented by time-series models such as AR(auto-regression). For time series, we may also obtain the expression in closed form, which has the advantage of being interpretable. Transferring from single to multiple dimensions increases the search space drastically. Thus, we must reduce it for the initial assumption search. The paper proposes the algorithm of algebraic expression discovery using evolutionary optimization. The initial assumption is made using the Fourier series decomposition. Several examples of the algorithm's work based on known functions and arctic ocean ice concentration data are shown.