Quality control and genetic algorithms: Difference between revisions

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The combination of '''quality control and genetic algorithms''' led to novel solutions of complex [[quality control]] design and [[Optimization (mathematics)|optimization]] problems. 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.<ref>Hoyle D. ISO 9000 quality systems handbook. Butterworth-Heineman 2001;p.654</ref> [[ISO 9000]] defines [[quality control]] as "A part of [[quality management]] focused on fulfilling quality requirements".<ref>ISO 9000:2005, Clause 3.2.10</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>

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{{Mergeto|Quality control|date=May 2009}}

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{{Notability|date=May 2009}}

'''Quality control''' is involved in developing systems to ensure [[product (business)|product]]s or [[Service (economics)|services]] are designed and produced to meet or exceed customer requirements or [[SLA]]'s. '''Genetic algorithms''' are search techniques, used in [[computing]] to find exact or [[approximate]] solutions to [[Optimization (mathematics)|optimization]] and search problems.

==Quality control==

Alternative [[quality control]]<ref>Duncan AJ. Quality control and industrial statistics. Irwin 1986;pp.1-1123.</ref> (QC) procedures can be applied onto a [[process]] to [[Statistical hypothesis testing|test]] statistically the [[null hypothesis]], that the process conforms to the quality specifications and consequently 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 forof 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 distributionprobability density function of errora inquality characteristic of the process. The probability forof rejection of a false [[null hypothesis]] isequals calledthe probability for errorof detection of the nonconformity of the process to the quality specifications.

The QC procedure to be designed or optimized can be formulated as:

:<math>Q_1 ( n_1,\mathbf{ X_1} ) \# Q_2 ( n_2,\mathbf{ X_2} ) \# ... \# Q_q (n_q,\mathbf{ X_q} )\;</math> (1)

''Q''₁(''n''₁,'''''X''₁''')# ''Q''₂(''n''₂,'''''X''₂''') #...# ''Q''_''q''(''n''_''q'','''''X''_''q''''') (1)

where ''Q''<sub>''i''</submath>Q_i (''n''_''i'' n_i,'''''X''<sub>''i''\mathbf{ X_i} )\;</submath>''')'''' denotes a statistical [[decision rule]], {{math|''n_i''}} denotes the size of the sample {{math|'''S'''_''i''}}, that is the number of the measurementssamples the rule is applied upon, and '''X'''<submath>''i''\mathbf{ X_i}\;</submath> denotes the vector of the rule specific parameters, including the decision limits. Each symbol {{math|''#''}} denotes either the [[Boolean operator (Boolean algebra)|Boolean operator]] operator AND or the operator OR. Obviously, for {{math|''#''}} denoting AND, and for {{math|''n''₁ < ''n''₂ <...< ''n''_''q''}}, that is for {{math|'''S'''₁ <math>\subset</math>⊂ '''S'''₂ <math>\subset</math>⊂ ....<math>\subset</math> ⊂ '''S'''_''q''}}, the (1) denotes a {{math|''q''}}-sampling QC procedure.

Each statistical decision rule is evaluated by calculating the respective statistic of the samplemeasured quality characteristic of the measurementssample. 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 (statistics)|range]] of the sample, the [[mean]], of the sample,and the [[standard deviation]] of the samplevalues 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 for errorof detection andof forthe falsenonconformity rejection.of Thethe probabilities for error detectionprocess and forof 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 errormonitored invariables of the process.

==Genetic algorithms==

In general, we can not use algebraic methods to optimize the QC procedures. Usage of [[enumerativeGenetic algorithms]] methods would be very tedious<ref>Holland, especiallyJH. withAdaptation multi-rulein procedures,natural asand theartificial numbersystems. of theThe pointsUniversity of theMichigan parameterPress space1975;pp.1-228.</ref><ref>Goldberg toDE. beGenetic searchedalgorithms growsin exponentiallysearch, withoptimization theand numbermachine oflearning. theAddison-Wesley parameters1989; topp.1-412.</ref><ref>Mitchell be optimizedM. [[OptimizationAn (mathematics)|Optimization]]Introduction methodsto based on the [[genetic algorithms]]. (GAs)The offerMIT anPress appealing alternative as they1998;pp.1-221.</ref> 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.

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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 [[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 (mathematics)|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,Kaufmann]] 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==

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==References==

{{reflist}}

{{Refimprove|date=May 2009}}

==External links==

* [https://asq.org American Society for Quality (ASQ)]

* [httphttps://www.hcsl.com Hellenic Complex Systems Laboratory (HCSL)]

[[Category:QualityStatistical process control]]

[[Category:Genetic algorithms]]