2 research outputs found
Systematically evolving configuration parameters for computational intelligence methods
Paper presented at The First International Conference (PReMI 2005); LNCS 3776: pp. 376-381.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
R.: Systematically Evolving Configuration Parameters for Computational Intelligence Methods
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.