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  • Оптимизация вычислений при применении генетических алгоритмов в задаче структурно-параметрической идентификации регрессионных моделей

Article

Оптимизация вычислений при применении генетических алгоритмов в задаче структурно-параметрической идентификации регрессионных моделей

Ахметсафина Р. З., Ахметсафин Р. Д.

The computationally efficient method of fitness function evaluation (criterion for chromosomes selection) in genetic algorithms (GA) is discussed in this paper. This method may be used if a single gene modifies chromosome.
Steiner's problem in graphs is solved for the computing optimization. Population is represented as a weighted graph. Vertices of that graph represent chromosomes, edges represent the computational cost of selection criteria recurrent calculation. The GA application for identification of regression models assumes (a) gene is a regressor;
(b) chromosome is the set of regressors in single regression model (subset of all candidates);
(c) population — set of regression models (subset of all possible models); (d) selection criteria — residual sum of squares (RSS); (e) the chromosome modification by modification of one gene corresponds to the forward selection and backward elimination methods of variables (regressors) selection.