Using Evolvable Regressors to Partition Data

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

@InProceedings{Brown:2010:ANNIE,

• author = "Joseph A. Brown and Daniel Ashlock",
• title = "Using Evolvable Regressors to Partition Data",
• booktitle = "ANNIE 2010, Intelligent Engineering Systems through Artificial Neural Networks",
• year = "2010",
• editor = "Cihan H. Dagli",
• volume = "20",
• pages = "187--194",
• address = "St. Louis, Mo, USA",
• month = nov # " 1-3",
• organisation = "Smart Engineering Systems Laboratory, Systems Engineering Graduate Programs, Missouri University of Science and Technology, 600 W. 14th St., Rolla, MO 65409 USA",
• publisher = "ASME",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "9780791859599",
• URL = "http://www.uoguelph.ca/~jbrown16/EvolRegress.pdf",
• URL = "https://asmedigitalcollection.asme.org/ebooks/book/149/chapter-abstract/30383/Using-Evolvable-Regressors-to-Partition-Data",
• DOI = "doi:10.1115/1.859599.paper24",
• abstract = "This manuscript examines permitting multiple populations of evolvable regressors to compete to be the best model for the largest number of data points. Competition between populations enables a natural process of specialisation that implicitly partitions the data. This partitioning technique uses function-stack based regressors and has the ability to discover the natural number of clusters in a data set via a process of sub-population collapse.",
• notes = "ASME Order Number: 859599",

}

Genetic Programming entries for Joseph Alexander Brown Daniel Ashlock

Citations