FlexGP
Name
10723_2014_9320_ReferencePDF.pdf
Size
1.38 MB
Format
Adobe PDF
Checksum (MD5)
336db44a888b8588e76f5178a9328acd
Author(s)
Veeramachaneni, Kalyan
Arnaldo, Ignacio
Derby, Owen
O’Reilly, Una-May
Date Issued
November 2014
Journal
Journal of Grid Computing
Publisher
Springer Netherlands
Citation
Veeramachaneni, Kalyan et al. “FlexGP: Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems.” Journal of Grid Computing 13.3 (2015): 391–407.
Version
Author's final manuscript
Abstract
We describe FlexGP, the first Genetic Programming system to perform symbolic regression on large-scale datasets on the cloud via massive data-parallel ensemble learning. FlexGP provides a decentralized, fault tolerant parallelization framework that runs many copies of Multiple Regression Genetic Programming, a sophisticated symbolic regression algorithm, on the cloud. Each copy executes with a different sample of the data and different parameters. The framework can create a fused model or ensemble on demand as the individual GP learners are evolving. We demonstrate our framework by deploying 100 independent GP instances in a massive data-parallel manner to learn from a dataset composed of 515K exemplars and 90 features, and by generating a competitive fused model in less than 10 minutes.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10723-014-9320-9
