Thesis Details
Využití evolučních algoritmů při učení neuronových sítí
Main point of this thesis is to find and compare posibilities of cooperation between evolutionary algorithms and neural network learning and their comparison with classical learning technique called backpropagation. This comparison is demonstrated with deep feed-forward neural network which is used for classification tasks. The process of optimalization is via search of optimal values of weights and biases within neural network with fixed topology. We chose three evolutionary approaches. Genetic algorithm, differential evolution and particle swarm optimization algorithm. These three approaches are also compared between each other. The demonstrating program is implemented in Python3 programming language without usage of any third parties libraries focused on deep learning.
neuroevolution, evolutionary algorithms, genetic algorithm, differential evolution, particle swarm optimization, neural network, deep learning, machine learning, Python
Fučík Otto, doc. Dr. Ing. (DCSY FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Szőke Igor, Ing., Ph.D. (DCGM FIT BUT), člen
Veselý Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT19256,
author = "David Vosol",
type = "Bachelor's thesis",
title = "Vyu\v{z}it\'{i} evolu\v{c}n\'{i}ch algoritm\r{u} p\v{r}i u\v{c}en\'{i} neuronov\'{y}ch s\'{i}t\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2019,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/19256/"
}