FlexGP

• Main Authors: Veeramachaneni, Kalyan (Contributor), Arnaldo, Ignacio (Contributor), Derby, Owen (Contributor), O'Reilly, Una-May (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
• Format: Article
• Language: English
• Published: Springer Netherlands, 2016-07-01T20:33:34Z.
• Subjects: Article
• Online Access: Get fulltext

 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.