Regularization partial description in evolutionary search for solutions at self-organization

Purpose. The paper deals with self-organizations method developed by A. G. Ivahnenko. For building mathematical model by this method it is necessary experimental data small amount. It is big advantage in complex systems study, including transient modes different processes. For constructing mathematical models by this method, there is full description of object model replaced by several partial descriptions. It well known that this descriptions complexity gives systems more accurate model. Partial description parameters may include nonlinear. There is proposing for determining these parameters using regularization. Methodology. There are general methods of mathematical models self-organization are gives in the article. Models construction is describe with using parameters that including nonlinear. There is unbiased criterion selected as criterion. Parameters obtaining is depends of input data division. It is propose divide experimental data point by random search. For determining partial description parameters using evolutionary search algorithm. Points random search process displayed in the algorithm. Findings. There is proposing self-organization method for mathematical method based on experimental data which contain evolutionary search algorithm for determining partial description parameters. Algorithm of this search is constructed. It differs from existing by random division of experimental points. Partial description parameters value is present as mathematical expectation. Originality. There is proposed new self-organization method for mathematical modeling which build partial description by evolutionary search in regularization process. Practical value. It is possible modeling quality increasing by this method of constructing mathematical model using. Especially it concerns of complex systems and processes that are more and more expose for modeling.