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Subject

Evolutionary Computing

1. Course Title Evolutionary Computing
Evolutionary computation
2. Code m23_s_032
3. Study Programme —
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) Second cycle
6. Academic year / semester 10 / Summer
7. Number of ECTS credits 6
8. Teacher Georgiina Mircheva, Miroslav Mirchev
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) The goal of the course is for students to become familiar with algorithms for computation and optimization inspired by evolutionary processes in nature, to be able to compare them, and to develop ideas for creating new techniques. Additionally, students will be equipped to apply the algorithms in various fields of computer engineering and science.
11. Course content Basic concepts and theory of evolutionary computation. Genetic algorithms. Evolutionary strategies. Evolutionary and genetic programming. Co-evolutionary algorithms and multi-objective optimization with evolutionary algorithms. Selection mechanisms. Evolutionary dynamics with game theory models. Evolutionary neural networks. Evolutionary development of sensor-robotic systems and network system design. Using evolutionary algorithms as models of real-world systems, as well as for discovering suitable models. Classification systems based on evolutionary algorithms. Swarm intelligence.
12. Learning methods Lectures supported by slide presentations, interactive lectures, practical exercises, teamwork, case studies, guest speakers, independent project work, and e-learning.
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 60 + 0 + 45 + 45 + 30 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 60 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 0 hours
16. Other forms of activities
16.1. Project assignments 45 hours
16.2. Independent assignments 45 hours
16.3. Home study 30 hours
17. Assessment method
17.1. Tests 30 points
17.2. Seminar paper / project (presentation: written and oral) 45 points
17.3. Activities and learning 10 points
17.4. Final exam 0 points
18. Grading criteria (points / grade)
up to 50 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam Activities 15 and 16 implemented
20. Language of instruction Macedonian and English
21. Method for monitoring the quality of teaching internal evaluation and survey mechanism
22. Literature
22.1. Required literature
1. A.E. Eiben, J.E. Smith | Introduction to Evolutionary Computing | Springer | 2015
2. David B. Fogel | Evolutionary Computation: Toward a New Philosophy of Machine Intelligence | John Wiley & Sons | 2006
3. De Jong, Kenneth A. | Evolutionary computation: a unified approach | MIT press | 2006
4. Jing Liu, Hussein A. Abbass, Kay Chen Tan | Evolutionary Computation and Complex Networks | Springer | 2019
5. Seyedali Mirjalili | Evolutionary Algorithms and Neural Networks: Theory and Applications | Springer | 2019
6. Editors Benjamin Doerr and Frank Neumann | Theory of Evolutionary Computation | Springer | 2020
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects