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FlexGP

delete2014-11-18
delete18
delete
OA
AI
K
Kalyan Veeramachaneni
I
Ignacio Arnaldo *
O
Owen Derby
U
Una-May O’Reilly
DOI：10.1007/s10723-014-9320-9delete
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Abstract

Abstract

En 中文
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.
Keywords:
Cloud computing
Ensemble learning
Genetic programming
Symbolic regression
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Journal

Journal of Grid Computing cover
Journal of Grid Computing
IF:
2.9
Papers:
759
Citations:
1.2K

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No organization information available