Computational intelligence techniques Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name: Computational intelligence techniques
Module code: W4-INA-S2-20-F-TIO
Programme code: W4-S2INA19.2021
Semester:
  • summer semester 2022/2023
  • winter semester 2022/2023
  • summer semester 2021/2022
Language of instruction: English
Form of verification: course work
ECTS credits: 4
Description:
The meta-heuristic algorithm can solve any problem that can be described with some terms defined by this algorithm. However, it is most often used to solve optimisation problems. A disadvantage of meta-heuristic algorithms is that they do not guarantee to find a solution, and it is usually impossible to give the time of their operation. The effectiveness of meta-heuristics also depends mostly on the parameters that appear in such algorithms. Unfortunately, there are no universal values of these parameters that behave best for all input data.
Prerequisites:
(no information given)
Key reading:
1. Arabas J., Lectures on evolutionary algorithms, Warsaw, 2004, WNT. 2. I. Rechenberg. Evolution strategy: Nature’s way of optimization. In Optimization: Methods and Applications, Possibilities and Limitations, pages 106-126. Springer Science & BusinessMedia, 1989. doi: 10.1007 / 978-3-642-83814-96. 3. Michalewicz Z., Genetic algorithms + data structures = evolutionary programs, Warsaw, 2001, WNT. 4. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic Publishers. ISBN 978-0-201-15767-3. 5. Glover, F .; Kochenberger, G.A. (2003). Handbook of metaheuristics. Springer, International Series in Operations Research & Management Science. ISBN 978-1-4020-7263-5.
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
The student knows advanced meta-heuristics and their applications in selected optimisation problems. [M_001]
 K_W01 [1/5] K_W02 [1/5]
The student can select a method to solve a presented optimisation problem. [M_002]
 K_U01 [1/5] K_U05 [1/5]
The student can write a program that implements selected meta-heuristics for optimising calculations. [M_003]
 K_U02 [1/5] K_U03 [1/5] K_U04 [1/5]
The student understands the need to develop decision-making methods for optimisation problems. [M_004]
 K_K01 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Test [W_001]
The student writes a test and describes existing techniques and their adaptation to selected optimisation problems.
 M_001
Programme related to the implemented project presentation. [W_002]
The student presents the programme and verifies its effectiveness for the selected optimisation problem.
 M_001 M_002 M_003
Multimedia presentation [W_003]
The student presents the advantages and disadvantages of the selected computational intelligence technique and tests it on a specific optimisation problem.
 M_001 M_002 M_004
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
The lectures combine verbal presentations with the use of content visualisation. They are focused on conceptually demanding material and refer to addresses of websites and e-learning package
 15
The students acquire knowledge from the lectures using the existing packages of methods: script, websites, and e-learning
 30 Test [W_001]
laboratory classes [Z_002]
Laboratory classes prepare students for implementing algorithms with an emphasis on the method and the sequence of operations.
 30
The student self-studies for the test from the laboratory classes. The students implement systems working in groups
 45 Test [W_001] Programme related to the implemented project presentation. [W_002] Multimedia presentation [W_003]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)