Metody genetického programování pro klasifikaci
| Thesis title in Czech: | Metody genetického programování pro klasifikaci |
|---|---|
| Thesis title in English: | Genetic programming methods for classification |
| Key words: | strojové učení|klasifikace|genetické programování |
| English key words: | machine learning|classification|genetic programming |
| Academic year of topic announcement: | 2023/2024 |
| Thesis type: | diploma thesis |
| Thesis language: | |
| Department: | Department of Theoretical Computer Science and Mathematical Logic (32-KTIML) |
| Supervisor: | Mgr. Roman Neruda, CSc. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 29.02.2024 |
| Date of assignment: | 16.03.2024 |
| Confirmed by Study dept. on: | 16.03.2024 |
| Date and time of defence: | 10.06.2024 09:30 |
| Date of electronic submission: | 02.05.2024 |
| Opponents: | Mgr. Martin Pilát, Ph.D. |
| Guidelines |
| Genetic programming (GP) is a collection of stochastic global optimization algorithms that operate on graph structures representing programs or formulae. As opposed to current deep learning models, GP methods produce models in the form of trees or direct acyclic graphs with understandable semantics, thus falling into the explainable AI category.
The goal of the thesis is to explore genetic programming approach in the context of supervised machine learning problems, namely classification tasks. The student will design and implement several GP algorithms tailored to operate on tabular data and both binary and multi-class classification problems. Variants of GP, such as tree-based syntactic trees and cartesian GP will be considered. The proposed algorithms will be implemented and tested on benchmark data sets. |
| References |
| Peter Flach (2012) Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, ISBN: 9780511973000. https://doi.org/10.1017/CBO9780511973000
Poli, Riccardo & Langdon, William & Mcphee, Nicholas. (2008). A Field Guide to Genetic Programming. http://www.gp-field-guide.org.uk Miller, Julian F. (ed). (2011). Cartesian Genetic Programming. Springer. ISBN: 978-3-642-17310-3. https://doi.org/10.1007/978-3-642-17310-3 Miller, Julian.F. Cartesian genetic programming: its status and future. Genetic Programming and Evolvable Machines 21, 129–168 (2020). https://doi.org/10.1007/s10710-019-09360-6 P. G. Espejo, S. Ventura and F. Herrera, "A Survey on the Application of Genetic Programming to Classification," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 2, pp. 121-144, March 2010, doi: 10.1109/TSMCC.2009.2033566. https://ieeexplore.ieee.org/abstract/document/5340522 |