Quality control and genetic algorithms: Difference between revisions

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'''[[Quality control]]''' is a set of activities intended to ensure that quality requirements are actually being met. Quality is the degree to which a set of inherent characteristics fulfils a need or expectation that is stated, general implied or obligatory<ref>Hoyle D. ISO 9000 quality systems handbook. Butterworth-Heineman 2001;p.654</ref>. '''[[Genetic algorithms]]''' are search algorithms, based on the mechanics of natural selection and natural genetics<ref>Goldberg DE. Genetic algorithms in search, optimization and machine learning. Addison-Wesley 1989; p.1.</ref>. The fruitful connection between '''quality control and genetic algorithms''' led to novel solutions of complex quality control design and optimization problems.

==Quality control==

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Each statistical decision rule is evaluated by calculating the respective statistic of a monitored variable of samples taken from the process. Then, if the statistic is out of the interval between the decision limits, the decision rule is considered to be true. 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. Finally, the QC procedure is evaluated as a [[Boolean]] proposition. If it is true, then the [[null hypothesis]] is considered to be false, the process is considered to be out of control, and the run is rejected.

A QC[[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. These probabilities depend on the parameters of the QC[[quality control]] procedure (1) and on the probability density functions (see [[probability density function]]) of the monitored variables of the process.

==Genetic algorithms==

In general, we can not use algebraic methods to optimize the QC procedures. Usage of [[enumerative]] methods would be very tedious, especially with multi-rule procedures, as the number of the points of the parameter space to be searched grows exponentially with the number of the parameters to be optimized. [[Optimization (mathematics)|Optimization]] methods based on the [[geneticGenetic algorithms]]<ref>Holland, JH. Adaptation in natural and artificial systems. The University of Michigan Press 1975;pp.1-228.</ref><ref>Goldberg DE. Genetic algorithms in search, optimization and machine learning. Addison-Wesley 1989; pp.1-412.</ref><ref>Mitchell M. An Introduction to genetic algorithms. The MIT Press 1998;pp.1-221.</ref> (GAs) offer an appealing alternative as they are robust search [[algorithms]], that do not require [[knowledge]] of the objective function to be optimized and search through large spaces quickly. GAs[[Genetic algorithms]] have been derived from the processes of the [[molecular biology]] of the [[gene]] and the [[evolution]] of life. Their operators, cross-over, [[mutation]], and [[reproduction]], are [[isomorphic]] with the synonymous biological processes. GAs[[Genetic algorithms]] have been used to solve a variety of complex [[Optimization (mathematics)|optimization]] problems. Furthermore,Additionally the complexity of the design process of novel QC procedures is obviously greater than the complexity of the [[Optimization (mathematics)|optimization]] of predefined ones. The classifier systems and the [[genetic programming]] [[paradigm]] have shown us that GAs[[Genetic algorithms]] can be used for tasks as complex as the program induction.

==Quality control and genetic algorithms==

In fact, since 1993, GAs have been used successfully to optimize and to design novel QC procedures<ref> Hatjimihail AT. Genetic algorithms based design and [[optimization]] of statistical quality control procedures. [[Clin Chem]] 1993;39:1972-8. [http://www.clinchem.org/cgi/reprint/39/9/1972]</ref><ref>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.</ref><ref>He D, Grigoryan A. Joint statistical design of double sampling x and s charts. European Journal of Operational Research 2006;168:122-142.</ref>.

In general, we can not use algebraic methods to optimize the [[quality control]] procedures. Usage of [[enumerative]] methods would be very tedious, especially with multi-rule procedures, as the number of the points of the parameter space to be searched grows exponentially with the number of the parameters to be optimized.[[Optimization (mathematics)|Optimization]] methods based on the [[genetic algorithms]] offer an appealing alternative. Furthermore, the complexity of the design process of novel [[quality control]] procedures is obviously greater than the complexity of the [[Optimization (mathematics)|optimization]] of predefined ones.

In fact, since 1993, GAs[[genetic algorithms]] have been used successfully to optimize and to design novel QC[[quality control]] procedures<ref> Hatjimihail AT. Genetic algorithms based design and [[optimization]] of statistical quality control procedures. [[Clin Chem]] 1993;39:1972-8. [http://www.clinchem.org/cgi/reprint/39/9/1972]</ref><ref>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.</ref><ref>He D, Grigoryan A. Joint statistical design of double sampling x and s charts. European Journal of Operational Research 2006;168:122-142.</ref>.

==See also==