Scaling simple, compact and extended compact genetic algorithms using MapReduce

MapReduce;Genetic Algorithms;Distributed Systems

Verma, Abhishek ; Campbell ; Roy H.

关键词: MapReduce;    Genetic Algorithms;    Distributed Systems;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/16856/1_Verma_Abhishek.pdf?sequence=3&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.