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We present the strategy of clustered genetic search as a case-oriented tool for global optimization problems. The strategy is performed in two quite different manners-as an iterative discrete hill-crunching approach and as a continuous method based on a finite mixture model. We set up the main points of theory on convergence, stop condition and error estimation. We illustrate the properties of both...
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