Infopaket

Evolutionary Computation

ВК2.7

Вибіркова дисципліна для ОП

First

2023/2024

1 Semester

5

Use the methods of computational intelligence, machine learning, neural network and fuzzy data processing, genetic and evolutionary programming to solve problems of recognition, forecasting, classification, identification of control objects, etc.Use the methods of computational intelligence, machine learning, neural network and fuzzy data processing, genetic and evolutionary programming to solve problems of recognition, forecasting, classification, identification of control objects, etc.

Full-time form

Know the basics of mathematical analysis, algebra and geometry, operations research, methods of artificial intelligence, algorithmization and programming, information systems design. Be able to search for information, solve optimization problems using classical methods, perform a comparative analysis of the effectiveness of the application of various models, methods and systems. Possess elementary skills of algorithmization of the process of solving applied problems and their formalization.

The study of the academic discipline is aimed at the acquisition by students of competencies in the field of solving problems of optimization of complex, non-smooth, polyextreme dependencies, which are models of various kinds of processes, using both applied analytical software and independently developed programs, which will allow future specialists to independently solve tasks of data processing under conditions of uncertainty. The program of the discipline "Evolutionary Computation" is built in such a way to teach students to analyze the surrounding processes, build their models, solve optimization problems, choosing effective methods for this, and perform analysis of the obtained solutions and, if necessary, make parametric adjustments.

7. Snytyuk V., Suprun O. Evolutionary clustering as technique of economic problems solving //Electronic and control Systems. – 2017. − No 4 (54). – P. 95-101. 8. Snytyuk V. (2020) Method of Deformed Stars for Multi-extremal Optimization. One- and Two-Dimensional Cases. In: Palagin A., Anisimov A., Morozov A., Shkarlet S. (eds) Mathematical Modeling and Simulation of Systems. MODS 2019. Advances in Intelligent Systems and Computing,vol 1019. Springer, Cham. 9. Snytyuk V., Antonevych M., Didyk A. Optimization of Functions of Two Variables by Deformed Stars Method // 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2019, pp. 475-480. 10. Snytyuk V., Tmienova N. Method of Deformed Stars for Global Optimization // 2020 IEEE 2nd International Conference on System Analysis & Intelligent Computing (SAIC), Kyiv, Ukraine, 2020, pp. 1-4.

Lectures, laboratory classes, individual study

The course includes 2 meaningful modules. Classes are held in the form of lectures, laboratory and independent work. The discipline ends with a test. Forms of evaluation: the level of achievement of all planned learning outcomes is determined by the results of writing written tests, performing laboratory, independent work. Assessment of students is carried out during the semester for all types of work. The total score for the semester is formed as a weighted sum of points earned by the student for various types of work. The maximum number of points that a student can receive for work in a semester does not exceed 80 points on a 100-point scale In the case of a student's absence due to valid reasons, practice and resubmission of test papers are carried out in accordance with the "Regulations on the procedure for assessing students' knowledge under the credit-module system of organization of the educational process" dated October 1, 2010.

Ukrainian