Development of a metaheuristic programming method for the nonlinear models synthesis

The solution of the problem of building nonlinear models (mathematical expressions, functions, algorithms, programs) based on an experimental data set, a set of variables, a set of basic functions and operations is considered. A metaheuristic programming method for the evolutionary synthesis of nonlinear models has been developed that has a representation of a chromosome in the form of a vector of real numbers and allows the use of various bioinspired (nature-inspired) optimization algorithms in the search for models. The effectiveness of the proposed algorithm is estimated using ten bioinspired algorithms and compared with a standard algorithm of genetic programming, grammatical evolution and Cartesian Genetic Programming. The experiments have shown a significant advantage of this approach as compared with the above algorithms both with respect to time for the solution search (greater than by an order of magnitude in most cases), and the probability of finding a given function (a model) (in many cases at a twofold rate).