The paper analyses a possible option for preparing data on the results of the genetic algorithm for transfer to another subject area. It was shown that the complexity of modern target functions requires the development of new approaches to determining the parameters of search procedures. A set of experiments, each stage of which consisted of performing 100 runs of the genetic algorithm on a CPU or GPU architecture, which determines the optimal solution of the Ackley’s function within a given time interval, was carried out. After the specified time interval expired, the operation of the algorithm was correctly completed by fixing the results obtained at the final iteration. The values of the absolute error were set to Δ={0.5, 0.15, 0.1, 0.05}. For each error value the number of algorithm runs, as a result of which the deviation was greater than Δ, was determined. On the basis of the experiment carried out, fuzzy estimates of the inexpediency of searching for the optimum of the Ackley’s function by the genetic algorithm on the CPU architecture in a time from 100 ms ...1800 ms were determined. The possibility of using intuitionistic fuzzy sets for describing the expediency of solving optimization problems by genetic algorithms with given parameters was shown.

We consider the task of comparing fuzzy estimates of the execution parameters of genetic algorithms implemented at GPU (graphics processing unit’ GPU) and CPU (central processing unit) architectures. Fuzzy estimates are calculated based on the averaged dependencies of the genetic algorithms running time at GPU and CPU architectures from the number of individuals in the populations processed by the algorithm. The analysis of the averaged dependences of the genetic algorithms running time at GPU and CPU-architectures showed that it is possible to process 10’000 chromosomes at GPU-architecture or 5’000 chromosomes at CPUarchitecture by genetic algorithm in approximately 2’500 ms. The following is correct for the cases under consideration: “Genetic algorithms (GA) are performed in approximately 2, 500 ms (on average), ” and a sections of fuzzy sets, with a = 0.5, correspond to the intervals [2, 000.2399] for GA performed at the GPU-architecture, and [1, 400.1799] for GA performed at the CPU-architecture. Thereby, it can be said that in this case, the actual execution time of the algorithm at the GPU architecture deviates in a lesser extent from the average value than at the CPU.

The problem under consideration consists in choosing the number of k individuals, so that the time for processing k individuals by the genetic algorithm (GA) on the CPU architecture is close to the time for processing l individuals on the GPU architecture by the genetic algorithm. The initial information is data arrays containing information about the processing time of a given number of individuals by the genetic algorithm on the available hardware architectures. Fuzzy numbers are determined based on these arrays?~? and?~?, describing the processing time of a given number of individuals, respectively, on the CPU and GPU architectures. The peculiarities of the subject area do not allow considering the well-known methods of comparison based on the equalities of the membership functions and the nearest clear sets as adequate. Based on the known formula “close to Y (around Y)” the way to compare fuzzy numbers?~? and?~? was developed in order to determine the degree of closeness of the processing time of k and l individuals, respectively, on the hardware architectures of the CPU and GPU.