doc. Ing. Jiří Kozumplík, CSc.

Department of Biomedical Engineering (UBMI)

The graduate of the course is capable of:
Implement a simple analytical optimization method (steepest descent and Newton's method)
To implement the simplex method for finding global extreme
Explain the nature of stochastic optimization methods with populations
Explain the nature of binary and continuous genetic algorithms and the basic operations

The knowledge on the Bachelor´s degree level is requested, namely on numerical mathematics. The laboratory work is expected knowledge of Matlab programming environment.

Not applicable.

Not applicable.

Tvrdík, J.: Evoluční algoritmy. Skripta, Přírodovědecká fakulta Ostravské univerzity, 2004 (CS)
Hynek, J.: Genetické algoritmy a genetické programování. Grada Publishing, 2008 (CS)
Zelinka a kol.: Evoluční výpočetní techniky. Principy a aplikace. BEN, Praha, 2009 (CS)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. John Wiley & Sons, New Jersey, 2004 (EN)

Teaching methods include lectures and computer laboratories. Course is taking advantage of e-learning system. Students have to write a single project/assignment during the course.

Requirements for completion of a course are elaborated by the lecturer responsible for the course 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)

Czech

Not applicable.

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.

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Basically:
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.

• Programme EEKR-M1 Master's

  branch M1-BEI , 2. year of study, winter semester, 5 credits, optional specialized
  

• Programme EEKR-CZV lifelong learning

  branch ET-CZV , 1. year of study, winter semester, 5 credits, optional specialized
  

13 hours, compulsory

doc. Ing. Jiří Kozumplík, CSc.