Scaling simple, compact and extended compact genetic algorithms using MapReduce
Verma, Abhishek
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https://hdl.handle.net/2142/16856
Description
Title
Scaling simple, compact and extended compact genetic algorithms using MapReduce
Author(s)
Verma, Abhishek
Issue Date
2010-08-20T17:59:58Z
Director of Research (if dissertation) or Advisor (if thesis)
Campbell, Roy H.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
MapReduce
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.
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