T-Man: Gossip-based fast overlay topology construction
Computer Networks: The International Journal of Computer and Telecommunications Networking
Scaling Genetic Algorithms Using MapReduce
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
EvAg: a scalable peer-to-peer evolutionary algorithm
Genetic Programming and Evolvable Machines
Scaling Populations of a Genetic Algorithm for Job Shop Scheduling Problems Using MapReduce
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
A peer-to-peer approach to genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Parallel learning to rank for information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Mahout in Action
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
A library to run evolutionary algorithms in the cloud using mapreduce
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Flex-GP: genetic programming on the cloud
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Learning regression ensembles with genetic programming at scale
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.