HCSL Publications
Genetic Algorithms Based Design and Optimization of Statistical Quality Control
1. Hatjimihail AT. Genetic algorithms based design and optimization of statistical quality control procedures. Clin Chem 1993;39:1972-8.
DOI: 10.1093/clinchem/39.9.1972. PMID: 8375083.
Abstract
In general, we cannot use algebraic or enumerative methods to optimize a quality control (QC) procedure so as to detect the total allowable analytical error with a stated probability, 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, I have developed two interactive computer programs, based on the deterministic crowding genetic algorithm. Given an analytical process, the program "Optimize" optimizes a user defined QC procedure, while the program "Design" designs a novel optimized QC procedure. The programs search through the parameter space and find the optimal or a near-optimal solution. The possible solutions of the optimization problem are evaluated using computer simulation.
Comment
This publication presents the first application of GAs in the field of statistical QC.
Full Text in Clinical Chemistry
2. Hatjimihail AT, Hatjimihail TT. Design of statistical quality control procedures using genetic algorithms. In LJ Eshelman (ed): Proceedings of the Sixth International Conference on Genetic Algorithms. San Francisco: Morgan Kauffman, 1995:551-7.
Abstract
In general, we cannot 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.
3. Hatjimihail AT, Hatjimihail TT. Design of statistical quality control procedures using genetic algorithms. arXiv:cs/0201024 [cs.NE].
DOI: 10.48550/arXiv.cs/0201024.
Abstract
In general, we cannot 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.