Quality is the degree to which a set of inherent characteristics of an entity fulfils a need or expectation that is stated, general implied or obligatory.
When a false null hypothesis is accepted, a statistical type II error is committed.
We fail then to detect a significant change in the probability density function of a quality characteristic of the process.
Many statistics can be used, including the following: a single value of the variable of a sample, the range, the mean, and the standard deviation of the values of the variable of the samples, the cumulative sum, the smoothed mean, and the smoothed standard deviation.
A quality control procedure is considered to be optimum when it minimizes (or maximizes) a context specific objective function.
The objective function depends on the probabilities of detection of the nonconformity of the process and of false rejection.
Genetic algorithms have been derived from the processes of the molecular biology of the gene and the evolution of life.
Optimization methods based on genetic algorithms offer an appealing alternative.
In fact, since 1993, genetic algorithms have been used successfully to optimize and to design novel quality control procedures.