Evolutionary Algorithms
- Credits: 4
- Ending: Examination
- Range: 0P + 2C
- Semester: summer
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
After studying this course, students gain the knowledge and should be able to:
A. Understanding the evolutionary principles of state-space solutions.
B. To be able to choose suitable representations of problems, to design effective coding schemes.
C. Apply appropriate genetic, hybrid, correction, and other operators, set the parameters of the evolutionary algorithm.
D. Orientation in IT tools and environments suitable for solving problems by evolutionary algorithms.
E. To be able to apply evolutionary algorithms to solve practical optimization problems.
F. Learn to communicate and work in a team to solve complex tasks.
Indicative content
1. State-space, state-space search, and search strategies
2. Heuristic state-space search algorithms and their relation to optimization problems
3. Evolutionary Darwin process and the importance of evolutionary algorithms
4. Genetic algorithm, basic concepts, state-space representation, state coding, parallel state-space search
5. Introduction to working with MATLAB software, examples of genetic algorithms
6. Blocks of genetic algorithm (selection, mutation, and crossing) and parameter setting
7. Genetic programming, types of genetic programs, and their implementation
8. The importance of genetic algorithms in obtaining knowledge from data
9. Parallel evolutionary techniques, coevolutionary algorithms cooperatively
10. Competitive coevolutionary algorithms
11. Evolutionary algorithms in artificial intelligence, in multi-agent systems
12. Work in teams on final projects
13. Presentation and defense of final projects
Support literature
Odporúčaná literatúra:
1. KVASNIČKA, V. -- POSPÍCHAL, J. -- TIŇO, P. Evolučné algoritmy. Bratislava : STU v Bratislave, 2000.. ISBN 80-227-1377-5
2. MACH, M. Evolučné algoritmy: Prvky a princípy. TU Košice, 2009. ISBN 978-80-8086-123-0
3. OPLATKOVÁ, Z., OŠMERA, P., ŠEDA, M., VČELAŘ, F., ZELINKA, I.: Evoluční výpočetní techniky - principy a aplikace. BEN - technická literatura, Praha, 2008, ISBN 80-7300-218-3
4. MICHALEWICZ, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Berlin: Springer Verlag, 1992, ISBN 978-3-540-60676-5
5. RUSSELL, S.J., NORVIG, P.: Artificial Intelligence, A Modern Approach, Prentice Hall, A Modern Approach, Global Edition, 2021
6. NEGNEVITSKY, M.: Artificial Intelligence: A Guide to Intelligent Systems (3nd Edition), Pearson Education Limited, 2011, ISBN-13: 978-1408225745
7. XINJIE, Y., MITSUO, G.: Introduction to Evolutionary Algorithms, Springer Verlag, ISBN 978-1-84996-128-8
8. EIBEN, A.E., SMITH, J.E, Introduction to Evolutionary Computing, 2nd ed. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-44873-1
9. Norvig, P., Russell, S., Artificial Intelligence: A Modern Approach, Global Edition, 2021
Requirements to complete the course
Requirements to complete the course:
- final exam - written form, 60% (passing the exam means obtaining 51% from the evaluation of exam) The exam consists of two parts: verification of theoretical knowledge (test with different types of questions). The theoretical part verifies the achieved level of educational results A, B, D. Verification of practical skills (work in MATLAB), where the level of educational results C, D, E is verified.
Seminars
- working in small teams: elaboration and seminar topic presentation 20%, work over the final project 20%
Together: 40%
By evaluating individual work and evaluating work in teams, the following educational results are developed and evaluated: B, C, E, F.
Student workload
Total study load (in hours):
4 credits x 26 hours= 104 hours
Study load distribution:
Seminar participation: 26 hours
Preparation for seminars: 10 hours
Project preparation: 24 hours
Preparation for the final exam: 44 hours
Language whose command is required to complete the course
slovak
Date of approval: 11.03.2024
Date of the latest change: 19.09.2023