Efficiency of parallelisation of genetic algorithms in the data analysis context
Perrin, Dimitri & Duhamel, Christophe (2013) Efficiency of parallelisation of genetic algorithms in the data analysis context. In IEEE 37th Annual Computer Software and Applications Conference Workshops (COMPSACW), IEEE Computer Society, Kyoto, Japan, pp. 339-344.
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of the datasets and the nature of the underlying mechanisms of the system under investigation. As datasets grow even larger, finding the balance between the quality of the approximation and the computing time of the heuristic becomes non-trivial. One solution is to consider parallel methods, and to use the increased computational power to perform a deeper exploration of the solution space in a similar time. It is, however, difficult to estimate a priori whether parallelisation will provide the expected improvement. In this paper we consider a well-known method, genetic algorithms, and evaluate on two distinct problem types the behaviour of the classic and parallel implementations.
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|Item Type:||Conference Paper|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 IEEE|
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|Deposited On:||09 Apr 2015 23:02|
|Last Modified:||13 Apr 2015 16:39|
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