FEKT-NEALAcad. year: 2019/2020
The course is focused on deterministic and stochastic optimization methods for finding global minima. It focuses on evolutionary algorithms with populations such as genetic algorithms, controlled random search, evolutionary strategies, particle swarm method, the method of ant colonies and more.
Learning outcomes of the course unit
Knowledge of the level of bachelor's degree and valid examination for qualification of personnel for independent activity (within the meaning of § 6 of Decree 50). The pre-intermediate knowledge of English are required.
Recommended optional programme components
Recommended or required reading
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. John Wiley & Sons, New Jersey, 2004 (EN)
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every year.
- 30 points can be obtained for activity in the laboratory exercises, consisting in solving tasks (for the procedure for the examination must be obtained at least 15 points)
- 70 points can be obtained for the written exam (the written examination is necessary to obtain at least 35 points)
Language of instruction
1. Optimization based on mathematical analysis, optimality conditions, gradient, Hessian
2. Method of steepest descent, Newton's method
3. Stochastic algorithms for finding global minima, the simplex method
4. Evolutionary algorithms with populations. Binary genetic algorithms.
5. Continuous genetic algorithms.
6. Controlled random search, evolutionary strategies, particle swarm
7. Differential evolution, SOMA, ant colony
8. Swarm algothms: BAT, FA, GSO.
9. Swarm algothms: GWO, BA, ABC.
10. Test function for checking optimization algorithms
11. Experimental comparison of evolutionary algorithms
12. Introduction to genetic programming
Obtaining an understanding about deterministic and stochastic optimization methods. Introduction to the evolutionary algorithms with populations for finding the global extremes multidimensional functions. Introduction to the genetic programming.