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Two references have been removed to avoid conflict of interest im. The remaining reference describeses the first application of the genetic algorithms in the field of QC, an obviously remarkable fact. |
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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,
==See also==
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