Publication at Faculty of Mathematics and Physics |
2016
Abstract
This paper describes an algorithm for the automated design of whole machine learning workflows, including preprocessing of the data and automatic creation of several types of ensembles. The algorithm is based on strongly typed genetic programming which ensures the validity of the workflows.
The evolution of the individuals in the population is asynchronous in order to improve the utilization of computational resources. The approach is validated on four data sets from the UCI machine learning repository.