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Title:Scaling simple, compact and extended compact genetic algorithms using MapReduce
Author(s):Verma, Abhishek
Advisor(s):Campbell, Roy H.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Genetic Algorithms
Distributed Systems
Abstract:Data-intensive computing has emerged as a key player for processing large volumes of data exploiting massive parallelism. Data-intensive computing frameworks have shown that terabytes and petabytes of data can be routinely processed. However, there has been little effort to explore how data-intensive computing can help scale evolutionary computation. We present a detailed step-by-step description of how three different evolutionary computation algorithms, having different execution profiles, can be translated into the MapReduce paradigm. Results show that (1) Hadoop is an excellent choice to push evolutionary computation boundaries on very large problems, and (2) that transparent linear speedups are possible without changing the underlying data-intensive flow thanks to its inherent parallel processing.
Issue Date:2010-08-20
Rights Information:Copyright 2010 Abhishek Verma
Date Available in IDEALS:2010-08-20
Date Deposited:2010-08

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