An approach to the acceleration of parametric weak classifier boosting is proposed. Weak classifier is called parametric if it has fixed number of parameters and, therefore, can be represented as a point in multidimensional space. Genetic algorithm is used to learn parameters of such classifier. Proposed approach also takes cases when effective algorithm for learning some of the classifier parameters exists into account. Experiments confirm that such an approach can dramatically decrease classifier training time while keeping both training and test errors small, at least for some widely used pattern recognition algorithms.

A genetic tests synthesis algorithm for the synchronous digital circuits, based on solving the integer optimization problem with a scalar objective function is presented. A decomposition strategy of the circuit under test and symbolic representation of its fragments obtained. New objective function with a global optimum which is the test sequence developed. Experimental results confirmed the effectiveness and practical applicability of the method.