Systematically Evolving Configuration Parameters for Computational Intelligence Methods

  • Jason M. Proctor
  • Rosina Weber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jason M. Proctor
    • 1
  • Rosina Weber
    • 1
  1. 1.College of Information Science & TechnologyDrexel University 

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