Scaling eCGA Model Building via Data-Intensive Computing

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Provided by: University of Illinois at Urbana Champaign
Topic: Storage
Format: PDF
In this paper, the authors show how the extended compact genetic algorithm can be scaled using data-intensive computing techniques such as MapReduce. Two different frameworks (Hadoop and MongoDB) are used to deploy MapReduce implementations of the compact and extended compact genetic algorithms. Results show that both are good choices to deal with large-scale problems as they can scale with the number of commodity machines, as opposed to previous efforts with other techniques that either required specialized high-performance hardware or shared memory environments.
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