Infopaket

Evolutional Сomputing

ОК 4

Обов’язкова дисципліна для ОП

Second

2022/2023

2 Semester

3

The student must know: general information about evolutionary computations, operation schemes of genetic algorithms, evolutionary strategies, differential evolution and genetic programming, basic operators of evolutionary computation algorithms, theoretical foundations of genetic algorithms. The student must have skills: to use the DEAP library of evolutionary computations to develop programs in the Python language that solve global optimization problems, to choose a method of encoding solutions, programmatically to construct a fitness function of typical tasks of evolutionary computations, to solve an asymmetric traveling salesman problem and the problem of linear cutting of material using the Python programming language.

Full-time form

The discipline “Data Analysis with Python” is based on such disciplines: “Programming”, “Higher Mathematics”, “Probability and Mathematical Statistics Theory”, “Algorithms and Methods of Calculation”, “Discrete Mathematics”.

The course "Evolutionary Computations" covers general information about evolutionary computing and its 4 main algorithms: genetic algorithms, evolutionary strategies, differential evolution and genetic programming. For each of the algorithms, the following material is presented: the scheme of the basic algorithm, the method of encoding solutions, operators of evolutionary algorithms (selection, crossover, mutation, and others), stopping criteria. The theoretical foundations of genetic algorithms are also considered.

1. Глибовець М.М., Гулаєва Н.М. Еволюційні алгоритми: підручник. – К.: НаУКМА, 2013. – 828 с. 2. S. Luke. Essentials of Metaheuristics. Second Edition. lulu.com, 2013. – 242 pages. 3. M. Locatelli, F. Schoen. Global Optimization: Theory, Algorithms, and Applications. Society for Industrial and Applied Mathematics, 2013. – 450 pages. 4. Застосування генетичних алгоритмів у комп'ютерних системах: монографія / С. Д. Погорілий, Р. В. Білоус, І. В. Білоконь; за ред. проф. С. Д. Погорілого. – К.: Видавничо-поліграфічний центр «Київський університет», 2014. – 319 с. 5. Субботін С.О., Олійник А.О., Олійник О.О. Неітеративні, еволюційні та мультиагентні методи синтезу нечіткологічних і нейромережних моделей: Монографія / Під заг. ред. С. О. Субботіна. – Запоріжжя: ЗНТУ, 2009. – 375 с.

Lectures, laboratory work, unsupervised work.

Semester assessment: the academic semester has two semantic modules. The first semantic module is assessed after the completion of lecture topics 8 by compiling electronic tests. The second semantic module is assessed by the results of compilation (defense) of 2 laboratory works. Final assessment (in the form of credit): the credit form is electronic testing. The total result of test is scored upto 40 points. The result of learning of the subject is considered positive if all 2 laboratory works is compilated (defensed) and the summary score for semantic modules and credit exceeds 60 points. Conditions of admission to the credit: the condition of admission to the credit is the student's receipt of a total of not less than the critical-calculated minimum of 20 points per semester.

Ukrainian