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Published in:
2013 | OriginalPaper | Chapter
Cloud Driven Design of a Distributed Genetic Programming Platform
Authors : Owen Derby, Kalyan Veeramachaneni, Una-May O’Reilly
Published in: Applications of Evolutionary Computation
Publisher: Springer Berlin Heidelberg
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We describe how we design FlexGP, a distributed genetic programming (GP) system to efficiently run on the cloud. The system has a decentralized, fault-tolerant, cascading startup where nodes start to compute while more nodes are launched. It has a peer-to-peer neighbor discovery protocol which constructs a robust communication network across the nodes. Concurrent with neighbor discovery, each node launches a GP run differing in parameterization and training data from its neighbors. This factoring of parameters across learners produces many diverse models for use in ensemble learning.