The presented article deals with the possibility of using artificial intelligence tools, specifically genetic algorithms, in the metallurgical industry. The genetic algorithms are used here to estimate coefficients of regression functions. In some cases, the standard regression analysis tools lead to incorrect results. And then the genetic algorithms can serve as an optimization tool searching for a certain state space to find functions or functionalities optimum. To verify the correctness of this premise we have used the genetic algorithms to find coefficients of two regression functions types, namely linear regression function and nonlinear regression function. By comparing the results, we have confirmed in the discussion the suitability of using genetic algorithms in the field of regression analysis as well.Another result of this work is the determination of the general cost equation of the foundry furnaces. The data of selected foundries have been used both in classical regression analysis and in genetic algorithms applications.Keywords: Cost Analysis, Genetic Algorithms, Metallurgical Industry.
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