Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{arnaldo:2014:EuroGP,
-
author = "Ignacio Arnaldo and Kalyan Veeramachaneni and
Una-May O'Reilly",
-
title = "Flash: A GP-GPU Ensemble Learning System for Handling
Large Datasets",
-
booktitle = "17th European Conference on Genetic Programming",
-
year = "2014",
-
editor = "Miguel Nicolau and Krzysztof Krawiec and
Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and
Juan J. Merelo and Victor M. {Rivas Santos} and
Kevin Sim",
-
series = "LNCS",
-
volume = "8599",
-
publisher = "Springer",
-
pages = "13--24",
-
address = "Granada, Spain",
-
month = "23-25 " # apr,
-
organisation = "EvoStar",
-
keywords = "genetic algorithms, genetic programming, GPU",
-
isbn13 = "978-3-662-44302-6",
-
DOI = "DOI:10.1007/978-3-662-44303-3_2",
-
abstract = "The Flash system runs ensemble-based Genetic
Programming (GP) symbolic regression on a shared memory
desktop. To significantly reduce the high time cost of
the extensive model predictions required by symbolic
regression, its fitness evaluations are tasked to the
desktop's GPU. Successive GP {"}instances{"} are run on
different data subsets and randomly chosen objective
functions. Best models are collected after a fixed
number of generations and then fused with an adaptive,
output-space method. New instance launches are halted
once learning is complete. We demonstrate that Flash's
ensemble strategy not only makes GP more robust, but it
also provides an informed online means of halting the
learning process. Flash enables GP to learn from a
dataset composed of 370K exemplars and 90 features,
evolving a population of 1000 individuals over 100
generations in as few as 50 seconds.",
-
notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
and EvoApplications2014",
- }
Genetic Programming entries for
Ignacio Arnaldo Lucas
Kalyan Veeramachaneni
Una-May O'Reilly
Citations