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PREMI
2005
Springer

Systematically Evolving Configuration Parameters for Computational Intelligence Methods

10 years 7 months ago
Systematically Evolving Configuration Parameters for Computational Intelligence Methods
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.
Jason M. Proctor, Rosina Weber
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PREMI
Authors Jason M. Proctor, Rosina Weber
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