Course detail

Evolution Algorithms

FEKT-MEALAcad. 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

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.

Recommended optional programme components

Not applicable.

Recommended or required reading

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)

Planned learning activities and teaching methods

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.

Assesment methods and criteria linked to learning outcomes

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)

Language of instruction


Work placements

Not applicable.

Course curriculum

1. Introduction to mathematical optimization, gradient, hessian.
2. Method of steepest descent, Newton method
3. Simplex method, hill climbing, tabu search, simulated annealing (SA), control random search (CRS), evolution search (ES).
4. Differential evolution (DE), evolutionary strategy (ES)
5. Genetic algorithms (GA), binary GA
6. Continuous GA, Travel salesman problem (TSP) and GA
7. Genetic programming
8. Ant colony (AC), TSP and AC, TST and SA
9. Partical swarm optimization (PSO)
10. Algorithms inspired by fireflies, bats, cuckoos
11. Algorithms inspired by wolves and bees
12. MATLAB optimization, algorithms verification and comparison


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.

Specification of controlled education, way of implementation and compensation for absences

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).
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.

Classification of course in study plans

  • 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

Type of course unit


Exercise in computer lab

13 hours, compulsory

Teacher / Lecturer

The other activities

13 hours, compulsory

Teacher / Lecturer