Evolutionary Algorithms

  • Credits: 4
  • Ending: Examination
  • Range: 0P + 2C
  • Semester: summer
  • Faculty of Economic Informatics

Teachers

Included in study programs

Information Management

Teaching results

Learning Outcomes after Course Completion
Knowledge
A. Understand evolutionary principles of searching the state space of solutions and their role in solving optimization problems.
B. Know the principles of problem representation and state encoding used in evolutionary algorithms.
Skills
B. Select appropriate problem representations and design efficient encoding schemes for evolutionary algorithms.
C. Apply genetic, hybrid, repair, and other evolutionary operators and appropriately set parameters of evolutionary algorithms.
D. Be proficient in using IT tools and software environments suitable for solving problems with evolutionary algorithms.
E. Apply evolutionary algorithms to the solution of practical optimization problems.
Competences
F. Collaborate effectively within a team when solving complex tasks, communicate efficiently, and take responsibility for shared outcomes.
E. Independently and responsibly apply evolutionary algorithms to solve complex optimization problems in practical application domains.

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

Syllabus

1. The course introduces the concept of the state space and methods for searching the state space. Basic search strategies and their properties are explained. 2. Heuristic algorithms for state space search are discussed. Their relationship to optimization problems and their role in improving search efficiency are emphasized. 3. The Darwinian evolutionary process is introduced together with its principles. The importance of evolutionary algorithms as computational optimization methods is explained. 4. The genetic algorithm is presented with emphasis on basic concepts. State space representation, state encoding, and parallel exploration of the search space are discussed. 5. An introduction to working with the MATLAB software environment is provided. Practical demonstrations of genetic algorithms implemented in MATLAB are presented. 6. Core components of genetic algorithms are discussed, including selection, mutation, and crossover. Attention is given to parameter setting and its impact on algorithm performance. 7. Genetic programming is introduced together with its main types. Principles of implementation and application of genetic programming techniques are discussed. 8. The importance of genetic algorithms in knowledge discovery from data is emphasized. Their role in pattern discovery and model optimization is explained. 9. Parallel evolutionary techniques are presented with emphasis on cooperative coevolutionary algorithms. Their principles and application scenarios are discussed. 10. Competitive coevolutionary algorithms are introduced. Differences between cooperative and competitive coevolution and their impact on evolutionary dynamics are explained. 11. The application of evolutionary algorithms in artificial intelligence and multi-agent systems is discussed. Emphasis is placed on adaptation, learning, and collective behavior. 12. Team-based work on final projects is addressed. Collaboration, problem formulation, and application of evolutionary techniques in practical tasks are emphasized. 13. The final part focuses on presentation and defense of final projects. Evaluation of solutions, communication of results, and critical discussion are emphasized.

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: 04.03.2025

Date of the latest change: 04.01.2026