Hatjimihail A T
Microbiology Laboratory, Health Center of Prosotsane, Greece.
Clin Chem. 1993 Sep;39(9):1972-8.
In general, one cannot use algebraic or enumerative methods to optimize a quality-control (QC) procedure for detecting the total allowable analytical error with a stated probability with the minimum probability for false rejection. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and can search through large parameter spaces quickly. To explore the application of GAs in statistical QC, I 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, whereas the program "Design" designs a novel optimized QC procedure. The programs search through the parameter space and find the optimal or near-optimal solution. The possible solutions of the optimization problem are evaluated with computer simulation.
一般来说，人们无法使用代数或枚举方法来优化质量控制（QC）程序，以便在规定概率下检测总允许分析误差，并使误拒收概率最小。遗传算法（GA）提供了一种替代方法，因为它们不需要了解要优化的目标函数，并且可以快速搜索大参数空间。为了探索遗传算法在统计质量控制中的应用，我基于确定性拥挤遗传算法开发了两个交互式计算机程序。给定一个分析过程，“优化”程序可优化用户定义的质量控制程序，而“设计”程序则设计一种新颖的优化质量控制程序。这些程序在参数空间中进行搜索并找到最优或近似最优解。优化问题的可能解通过计算机模拟进行评估。
