Design of statistical quality control procedures using genetic algorithms

TL;DR

This paper presents a genetic algorithm-based method for designing statistical quality control procedures that optimize error detection while minimizing false rejections, outperforming existing methods in clinical laboratory settings.

Contribution

It introduces a novel GA-based approach and a computer program for near-optimal QC procedure design, demonstrating improved performance over traditional methods.

Findings

The GA-based method outperforms 45 existing QC procedures.

The developed program effectively designs near-optimal QC procedures.

Application shows potential for improved error detection in clinical labs.

Abstract

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.