Quality control and genetic algorithms: Difference between revisions

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Revision as of 14:47, 19 May 2009

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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 [[genetic algorithms]] (GAs) offer an appealing alternative as they are robust search [[algorithms]], that do not require [[knowledge]] of the objective function and search through large spaces quickly. GAs 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 have been used to solve a variety of complex [[Optimization (mathematics)|optimization]] problems. Furthermore, 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 can be used for tasks as complex as the program induction.

In fact, in 1993, GAs were successfully used 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>

==See also==