Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{journals/ijait/KrenPN17,

• author = "Tomas Kren and Martin Pilat and Roman Neruda",
• title = "Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming",
• journal = "International Journal on Artificial Intelligence Tools",
• year = "2017",
• number = "5",
• volume = "26",
• pages = "1--24",
• month = oct,
• note = "Special Issue on Selected Papers from the 28th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2016); Guest Editors: Amol Mali and Miltos Alamaniotis",
• keywords = "genetic algorithms, genetic programming, STGP, machine learning workflows, asynchronous evolutionary algorithm",
• ISSN = "0218-2130",
• bibdate = "2017-11-22",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijait/ijait26.html#KrenPN17",
• DOI = "doi:10.1142/S021821301760020X",
• 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 workows (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 workows. 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.",

}

Genetic Programming entries for Tomas Kren Martin Pilat Roman Neruda

Citations