Quality control and genetic algorithms

The combination of quality control and genetic algorithms led to novel solutions of complex quality control design and optimization problems. Quality control is a process by which entities review the quality of all factors involved in production. Quality is the degree to which a set of inherent characteristics fulfils a need or expectation that is stated, general implied or obligatory. Genetic algorithms are search algorithms, based on the mechanics of natural selection and natural genetics. The combination of quality control and genetic algorithms led to novel solutions of complex quality control design and optimization problems. Quality control is a process by which entities review the quality of all factors involved in production. Quality is the degree to which a set of inherent characteristics fulfils a need or expectation that is stated, general implied or obligatory. Genetic algorithms are search algorithms, based on the mechanics of natural selection and natural genetics. Alternative quality control (QC) procedures can be applied on a process to test statistically the null hypothesis, that the process conforms to the quality requirements, therefore that the process is in control, against the alternative, that the process is out of control. When a true null hypothesis is rejected, a statistical type I error is committed. We have then a false rejection of a run of the process. The probability of a type I error is called probability of false rejection. When a false null hypothesis is accepted, a statistical type II error is committed. We fail then to detect a significant change in the process. The probability of rejection of a false null hypothesis equals the probability of detection of the nonconformity of the process to the quality requirements.