NiaAML: AutoML framework based on stochastic population-based nature-inspired algorithms
Luka Pečnik, Iztok Fister · The Journal of Open Source Software · 2021
The field of Automated Machine Learning (AutoML) has been developed to automate data preprocessing and search for optimal algorithms together with their hyperparameters in order to discover the best possible ML pipeline for an input dataset (Hutter et al., 2019).AutoML can be modeled as a continuous optimization problem with several potential optimization methods considered.Stochastic population-based nature-inspired algorithms (Engelbrecht, 2007;Yang, 2014) are a popular class of tools for dealing with such continuous optimization problems.These algorithms are inspired mainly by the biological behavior of various species living in nature (Fister Jr et al., 2013).Such algorithms are composed of a population of individuals that undergo different variation operations during the evolution process which results in new populations.The Python framework we have developed, NiaAML, incorporates these stochastic algorithms to search for the most suitable classification pipeline in a dataset (Fister Jr. et al., 2020).