Paper Info
Title
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.
Year
DOI
Venue
2005
10.1007/11590316_57
PReMI
Keywords
Field
DocType
computational intelligence,wide variety,suitable configuration,integrated ci system,genetic algorithm,computational intelligence method,ci method,software testing,different input data set,accurate result,alternate configuration parameter value
Data set,Computational intelligence,Computer science,Artificial intelligence,Artificial neural network,Genetic algorithm,Machine learning,Software testing
Conference
Volume
ISSN
ISBN
3776
0302-9743
3-540-30506-8
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
Jason M. Proctor1505.10
Rosina Weber233434.42