English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

e3s web of conferences

On the issue of using intuitionistic fuzzy sets for describing the expediency of solving optimization problems by genetic algorithms with given parameters