doc. Ing. Jiří Kozumplík, CSc.

Department of Biomedical Engineering (UBMI)

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Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. John Wiley & Sons, New Jersey, 2004 (EN)

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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)

English

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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.

• Programme EEKR-MN Master's

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

13 hours, compulsory

doc. Ing. Jiří Kozumplík, CSc.

eLearning: currently opened course