Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Pilat:2016:ICTAI,

• author = "Martin Pilat and Tomas Kren and Roman Neruda",
• booktitle = "2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)",
• title = "Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming",
• year = "2016",
• pages = "577--584",
• abstract = "This paper describes an algorithm for the automated design of whole machine learning work-flows, 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 usage of computational resources. The approach is validated on four data sets from the UCI machine learning repository.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICTAI.2016.0094",
• month = nov,
• notes = "Also known as \cite{7814654}",

}

Genetic Programming entries for Martin Pilat Tomas Kren Roman Neruda

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