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Evolutionary Approaches to Big-Data Problems

MIT News (01/14/15) Eric Brown

The Massachusetts Institute of Technology’s (MIT) AnyScale Learning For All (ALFA) group investigates a wide range of big data challenges. ALFA focuses on working with raw data that comes directly from the source and then investigates the data with a variety of techniques, most of which involve scalable machine learning and evolutionary computing algorithms. “Machine learning is very useful for retrospectively looking back at the data to help you predict the future,” says ALFA director Una-May O‚ÄôReilly. “Evolutionary computation can be used in the same way, and it’s particularly well suited to large-scale problems with very high dimensions.” Within the evolutionary field, O’Reilly has particular interest in genetic programing. “We distribute the genetic programming algorithms over many nodes and then factor the data across the nodes,” she says. The researchers have shown ensemble-based models are more accurate than a single model based on all the data. One of ALFA’s most successful projects has been in developing algorithms to help design wind farms. “You must find out how much wind is required for the site and then acquire the finer detailed information about where the wind is coming from and in what quantities,” O’Reilly says. The researchers also are trying to discover useful information from the growing volume of physiological data collected from medical sensors.

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