УДК 004.738 A.G. YUSHCHENKO, Ph. D., Prof., NTU «KhPI» D.A. PASHKO, magister , NTU «KhPI» O.V. GATILOVA, specialist, NTU «KhPI» EVOLUTIONARY STRATEGIES OF A GENETIC ALGORITHM FOR A TRAVELING SALESMAN AND NEW YORK TAXI DRIVER PROBLEMS The paper presents the original results of a study of evolutionary strategies in the application of genetic algorithms, based on software built by the researchers of Duke University. The proposed strategy is to pre-decompose the general problem into two sub-tasks, each of which, in the first step is solving the classical genetic circuit separately, and then, at a certain stage of optimization, returns to the solution of the original. Besides, new genotypes have been formed from two pre-optimized. The subject of a research was finding a balance between the "degree of optimization" of the first and second stages. The numerical simulations show that the strategy of dividing the route into two can accelerate the search for the optimal path. It has been found that the effectiveness of the strategy depends on the degree of decomposition and optimization of the combined phases of solving the problem, the number and location of cities, and the ratio of the number of generations of selection in the first and second stages is crucial, regardless of the number of cities and their placement. Keywords: Artificial Intelligence; Evolutionary strategies; Genetic algorithms; Decomposition; Travelling Salesman Problem. Introduction. The creative nature of biological evolution is demonstrated not only by the fact that it creates a surprising variety of organisms that can impress us with its phenotypic originality [1], but the fact of homological creativity of nature and the individual [2,3]. Biological evolution in the mathematical sense is the result of a phenotypes optimization period that took more than 4 billion years in the space of our planet by means of mutation genotypes. A fundamental feature of the process of evolution is compatible co-evolutionary organisms in ecosystems, because each of them other representatives of the biosphere play the role of the so-called "Cost function", which itself is subject to change due to mutation genome plasticity. Consideration of organisms as effective "solvers" problems of survival [4],