Using regression to improve local convergence

Bird, S and Li, X 2007, 'Using regression to improve local convergence', in K. Tan (ed.) Congress on Evolutionary Computation, Singapore, 25-28 September 2007, pp. 592-599.


Document type: Conference Paper
Collection: Conference Papers

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Title Using regression to improve local convergence
Author(s) Bird, S
Li, X
Year 2007
Conference name Congress on Evolutionary Computation
Conference location Singapore
Conference dates 25-28 September 2007
Proceedings title Congress on Evolutionary Computation
Editor(s) K. Tan
Publisher IEEE
Place of publication Piscataway, USA
Start page 592
End page 599
Total pages 8
Abstract Traditionally Evolutionary Algorithms (EAs) choose candidate solutions based on their individual fitnesses, usually without directly looking for patterns in the fitness landscape discovered. These patterns often contain useful information that could be used to guide the EA to the optimum. While an EA is able to quickly locate the general area of a peak, it can take a considerable amount of time to refine the solution to accurately reflect its true location. We present a new technique that can be used with most EAs. A surface is fitted to the previously-found points using a least squares regression. By calculating the highest point of this surface we can guide the EA to the likely location of the optimum, vastly improving the convergence speed. This technique is tested on Moving Peaks, a commonly used dynamic test function generator. It was able to significantly outperform the current state of the art algorithm.
Subjects Optimisation
Keyword(s) dynamic optimization
swarm intelligence
DOI - identifier 10.1109/CEC.2007.4424524
Copyright notice © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 978-1-4244-1339-3
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