Attribute reduction for massive data based on rough set theory and MapReduce
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
Iterative optimization for the data center
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
A library to run evolutionary algorithms in the cloud using mapreduce
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Distributed simulated annealing with 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
Breaking the MapReduce stage barrier
Cluster Computing
Cloud driven design of a distributed genetic programming platform
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Cloud scale distributed evolutionary strategies for high dimensional problems
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
High performance parallel evolutionary algorithm model based on MapReduce framework
International Journal of Computer Applications in Technology
An improved partitioning mechanism for optimizing massive data analysis using MapReduce
The Journal of Supercomputing
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Genetic algorithms(GAs) are increasingly being applied to large scale problems. The traditional MPI-based parallel GAs require detailed knowledge about machine architecture. On the other hand, MapReduce is a powerful abstraction proposed by Google for making scalable and fault tolerant applications. In this paper, we show how genetic algorithms can be modeled into the MapReduce model. We describe the algorithm design and implementation of GAs on Hadoop, an open source implementation of MapReduce. Our experiments demonstrate the convergence and scalability up to 10^5 variable problems. Adding more resources would enable us to solve even larger problems without any changes in the algorithms and implementation since we do not introduce any performance bottlenecks.