## 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.

#### Abstract

In general, we can not 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.

Abstract in PubMed

Full text in Clinical Chemistry

### 2. Hatjimihail AT, Hatjimihail TT. Design of
statistical quality control procedures using genetic algorithms [HCSL Technical
Report No II]. Drama: Hellenic Complex Systems Laboratory, 1994.

#### 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.

Abstract in arXiv.org e-Print archive

Full
text in arXiv.org e-Print archive

### 3. 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 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.