Publication at Faculty of Mathematics and Physics |
2017
Abstract
Manual creation of machine learning ensembles is a hard and tedious task which requires an expert and a lot of time. In this work we describe a new version of the GP-ML algorithm which uses genetic programming to create machine learning workflows (combinations of preprocessing, classification, and ensembles) automatically, using strongly typed genetic programming and asynchronous evolution.
The current version improves the way in which the individuals in the genetic programming are created and allows for much larger workflows. Additionally, we added new machine learning methods.
The algorithm is compared to the grid search of the base methods and to its previous versions on a set of problems from the UCI machine learning repository.