Thesis Details
Evoluční návrh neuronových sítí využívající generativní kódování
The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datasets. The method is also able to develop neural networks capable of solving multiple problems at once while slightly reducing accuracy.
neural network, evolutionary algorithms, genetic programming, cartesian genetic programming, evolutionary development of neural network
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Peringer Petr, Dr. Ing. (DITS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
Smrčka Aleš, Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT22348, author = "Tereza Hytychov\'{a}", type = "Master's thesis", title = "Evolu\v{c}n\'{i} n\'{a}vrh neuronov\'{y}ch s\'{i}t\'{i} vyu\v{z}\'{i}vaj\'{i}c\'{i} generativn\'{i} k\'{o}dov\'{a}n\'{i}", school = "Brno University of Technology, Faculty of Information Technology", year = 2021, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/22348/" }