Final thesis detail
||Coevolutionary Algorithm for Test-Based Problems
||Ing. Jiří Hulva
||Ing. Michaela Drahošová
||The opponent will be displayed after his opinion is published.
||Faculty of Information Technology
||Bioinformatics and biocomputing
||Defended (thesis was successfully defended)
Characteristics of thesis dilemmas:|
Objectives which should be achieve:|
Coevolutionary algorithms, symbolic regression, evolutionary algorithms, cartesian genetic programming
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.
- 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.
Tip: a short reference to the final thesis is also: https://www.vutbr.cz/en/studies/final-thesis?zp_id=79747
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