Evolutionary Partitioning Regression with Function Stacks

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

@InProceedings{Ashlock:2016:CEC,

• author = "Daniel A. Ashlock and Joseph Alexander Brown",
• title = "Evolutionary Partitioning Regression with Function Stacks",
• booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)",
• year = "2016",
• editor = "Yew Song Ong",
• pages = "1469--1476",
• address = "Vancouver",
• month = "25-29 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-5090-0623-6",
• DOI = "doi:10.1109/CEC.2016.7743963",
• size = "8 pages",
• abstract = "Partitioning regression is the simultaneous fitting of multiple models to a set of data and partitioning of that data into easily modelled classes. The key to partitioning regression with evolution is minimum error assignment during fitness evaluation. Assigning a point to the model for which it has the least error while using evolution to minimize total model error encourages the evolution of models that cleanly partition data. This study demonstrates the efficacy of partitioning regression with two or three models on simple bivariate data sets. Possible generalizations to the general case of clustering are outlined.",
• notes = "CEC2016 WCCI2016",

}

Genetic Programming entries for Daniel Ashlock Joseph Alexander Brown

Citations