Abstract
The solution of the problem of building nonlinear models (mathematical expressions, functions, algorithms, and programs) based on an experimental data set, a set of variables, or a set of basic functions and operations is considered. A metaheuristic programming method for the evolutionary synthesis of nonlinear models has been developed; in this method, a chromosome is represented in the form of a vector of real numbers and allows the use of various bio-inspired (nature-inspired) optimization algorithms in the search for models. The efficiency of the proposed algorithm is estimated using ten bio-inspired algorithms (two modifications of the genetic programming algorithm, differential evolution algorithm, particle swarm optimization algorithm, artificial bee colony algorithm, teaching-learning-based optimization algorithm and two of its modifications, covariance matrix adaptation evolution strategy, and simultaneous heat transfer search algorithm) and compared with the results of standard algorithms of genetic programming, grammatical evolution, and Cartesian genetic programming. The experiments have shown a significant advantage of this approach as compared to the above-mentioned algorithms in terms of both the time needed for finding the solution (greater than by an order of magnitude in most cases) and the probability of finding a given function (model) (greater than twice in many cases).
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