Thesis Details
Genetické programování v úlohách predikce
This thesis introduces various machine learning algorithms which can be used in prediction tasks based on regression. Tree genetic programming and linear genetic programming are explained more thoroughly. Selected machine learning algorithms (linear regression, random forest, multilayer perceptron and tree genetic programming) are compared on publicly available datasets with the use of scikit-learn and gplearn libraries. A core part of this project is a new implementation of linear genetic programming which was developed in C++, tested on common symbolic regression problems and then evaluated on real datasets. Results obtained with the proposed system are compared with the results obtained with gplearn.
genetic programming, linear genetic programming, machine learning, linear regression, random forest, multilayer perceptron, regression, Python, C++, scikit-learn, gplearn
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT22349, author = "Michal Macha\v{c}", type = "Bachelor's thesis", title = "Genetick\'{e} programov\'{a}n\'{i} v \'{u}loh\'{a}ch predikce", school = "Brno University of Technology, Faculty of Information Technology", year = 2020, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/22349/" }