Thesis Details
Evoluční algoritmy v návrhu konvolučních neuronových sítí
This work focuses on automatization of neural network design via the so-called neuroevolution, which employs evolutionary algorithms to construct artificial neural networks or optimise their parameters. The goal of the project is to design and implement an evolutionary algorithm which can be used in the process of designing and optimizing topologies of convolutional neural networks. The effectiveness of the proposed framework was experimentally evaluated on tasks of image classification on datasets MNIST and CIFAR10 and compared with relevant solutions. The results showed that neuroevolution has a potential to successfully find accurate and effective convolutional neural network architectures.
evolutionary algorithms, genetic algorithms, convolutional neural network, neuroevolution
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Vojnar Tomáš, prof. Ing., Ph.D. (DITS FIT BUT), člen
Vranić Valentino, doc. Ing., Ph.D. (FIIT STU), člen
@mastersthesis{FITMT22007,
author = "Filip Bad\'{a}\v{n}",
type = "Master's thesis",
title = "Evolu\v{c}n\'{i} algoritmy v n\'{a}vrhu konvolu\v{c}n\'{i}ch 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/22007/"
}