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 problems. '''[[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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==Quality control and genetic algorithms==

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, [[genetic algorithms]] have been used successfully to optimize and to design novel [[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>.