Computer Science – Neural and Evolutionary Computing
Scientific paper
2002-01-27
LJ Eshelman (ed): Proceedings of the Sixth International Conference on Genetic Algorithms. San Francisco: Morgan Kauffman, 199
Computer Science
Neural and Evolutionary Computing
11 pages, 1 figure
Scientific paper
In general, we can not use algebraic or enumerative methods to optimize a quality control (QC) procedure so as to detect the critical random and systematic analytical errors with stated probabilities, while the probability for false rejection is minimum. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and search through large parameter spaces quickly. To explore the application of GAs in statistical QC, we have developed an interactive GAs based computer program that designs a novel near optimal QC procedure, given an analytical process. The program uses the deterministic crowding algorithm. An illustrative application of the program suggests that it has the potential to design QC procedures that are significantly better than 45 alternative ones that are used in the clinical laboratories.
Hatjimihail Aristides T.
Hatjimihail Theophanes T.
No associations
LandOfFree
Design of statistical quality control procedures using genetic algorithms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Design of statistical quality control procedures using genetic algorithms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Design of statistical quality control procedures using genetic algorithms will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-420720