- author = "Kalyan Veeramachaneni and Ignacio Arnaldo and Owen Derby and Una-May O'Reilly",
- title = "{FlexGP} - Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems",
- journal = "Journal of Grid Computing",
- year = "2015",
- number = "3",
- volume = "13",
- bibdate = "2015-12-10",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/grid/grid13.html#VeeramachaneniA15",
- pages = "391--407",
- keywords = "genetic algorithms, genetic programming, MRGP, ARM, gossip protocol, Matlab, NSGA-II, ANN, seeding, Cloud computing, Ensemble learning, Symbolic regression",
-
URL = "
https://dspace.mit.edu/bitstream/handle/1721.1/103516/10723_2014_9320_ReferencePDF.pdf",
-
URL = "
http://dx.doi.org/10.1007/s10723-014-9320-9",
-
DOI = "
doi:10.1007/s10723-014-9320-9",
- size = "17 pages",
- 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. Flex-GP 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.",
-
notes = "DataModeler \cite{Friese:2012:dortmund} and Eureqa
\cite{Science09:Schmidt} Data Modeller, Eurequa,
embarrassingly parallel, LASSO, factor subsets,
NSGA-II,regularized linear regression, Vopal Wabbit,
million song dataset DynEq GP, producer
effect
https://flexgp.github.io/flexgp/",
