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Systematically evolving configuration parameters for computational intelligence methods
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/2039
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| Title: | Systematically evolving configuration parameters for computational intelligence methods |
| Authors: | Proctor, Jason M.
Weber, Rosina O. |
| Issue Date: | 2005 |
| Publisher: | Springer Verlag |
| Citation: | Paper presented at The First International Conference (PReMI 2005); LNCS 3776: pp. 376-381. |
| Abstract: | The configuration of a computational intelligence (CI) method is
responsible for its intelligence (e.g. tolerance, flexibility) as well as its
accuracy. In this paper, we investigate how to automatically improve the
performance of a CI method by finding alternate configuration parameter values
that produce more accurate results. We explore this by using a genetic
algorithm (GA) to find suitable configurations for the CI methods in an
integrated CI system, given several different input data sets. This paper
describes the implementation and validation of our approach in the domain of
software testing, but ultimately we believe it can be applied in many situations
where a CI method must produce accurate results for a wide variety of
problems. |
| URI: | http://dx.doi.org/10.1007/11590316_57
http://hdl.handle.net/1860/2039 |
| Appears in Collections: | Faculty Research and Publications (IST)
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