Computational intelligence as an emerging paradigm of software engineering
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Breeding Software Test Cases with Genetic Algorithms
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
The data mining approach to automated software testing
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Architectural overview of the computational intelligence testing tool
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
CBR for modeling complex systems
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
CBR for modeling complex systems
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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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.