• Brno University of Technology - Centre of Sports Activities
  • Research centres

  • Pravděpodobně máte vypnutý JavaScript. Některé funkce portálu nebudou funkční.

Final theses

Faculty Year
Language of thesis
Type of work
Searched text
 search keywords  search in thesis author
 search in title work  search in thesis leader
 look at abstract  
Note
- the search is case and diacritics insensitive
- use the '*' wild-card character to replace a portion of a string

Final thesis detail

Title: Coevolutionary Algorithm for Test-Based Problems
Type: Master's Thesis
Year: 2013/2014
Student: Ing. Jiří Hulva
Leader: Ing. Michaela Drahošová
Opponent: The opponent will be displayed after his opinion is published.
Faculty: Faculty of Information Technology
Department: UPSY
Study branch: Bioinformatics and biocomputing
Language: Czech
State: Defended (thesis was successfully defended)
Characteristics of thesis dilemmas:
Not applicable.
Objectives which should be achieve:
Not applicable.
Keywords:
Coevolutionary algorithms, symbolic regression, evolutionary algorithms, cartesian genetic programming
Abstract:
This thesis deals with the usage of coevolution in the task of symbolic regression. Symbolic regression is used for obtaining mathematical formula which approximates the measured data. It can be executed by genetic programming - a method from the category of evolutionary algorithms that is inspired by natural evolutionary processes. Coevolution works with multiple evolutionary processes that are running simultaneously and influencing each other. This work deals with the design and implementation of the application which performs symbolic regression using coevolution on test-based problems. The test set was generated by a new method, which allows to adjust its size dynamically. Functionality of the application was verified on a set of five test tasks. The results were compared with a coevolution algorithm with a fixed-sized test set. In three cases the new method needed lesser number of generations to find a solution of a desired quality, however, in most cases more data-point evaluations were required.
Literature:
  • Popovici, Elena, et al. "Coevolutionary principles." Handbook of Natural Computing. Springer Berlin Heidelberg, 2012. 987-1033.
  • Šikulová, M., Sekanina, L.: Coevolution in Cartesian Genetic Programming, In: Proc. of the 15th European Conference on Genetic Programming, Heidelberg, DE, Springer, 2012, s. 182-193
  • Dle pokynů vedoucí práce.
Reason for concealment:
Not applicable.

File inserted by student Size Public version
Main document [.pdf] 1.22 MB yes

Tip: a short reference to the final thesis is also: https://www.vutbr.cz/en/studies/final-thesis?zp_id=79747

Back to list