The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AN2302H1D" target="_blank" >RIV/61988987:17310/22:N2302H1D - isvavai.cz</a>

Alternative codes found

RIV/61988987:17310/22:A2302H1D

Result on the web

<a href="https://www.mdpi.com/2073-431X/11/5/70" target="_blank" >https://www.mdpi.com/2073-431X/11/5/70</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/computers11050070" target="_blank" >10.3390/computers11050070</a>

Result language

angličtina

Original language name

The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks

Original language description

The presented research study focuses on demonstrating the learning ability of a neural network using a genetic algorithm and finding the most suitable neural network topology for solving a demonstration problem. The network topology is significantly dependent on the level of generalization. More robust topology of a neural network is usually more suitable for particular details in the training set and it loses the ability to abstract general information. Therefore, we often design the network topology by taking into the account the required generalization, rather than the aspect of theoretical calculations. The next part of the article presents research whether a modification of the parameters of the genetic algorithm can achieve optimization and acceleration of the neural network learning process. The function of the neural network and its learning by using the genetic algorithm is demonstrated in a program for solving a computer game. The research focuses mainly on the assessment of the influence of changes in neural networks’ topology and changes in parameters in genetic algorithm on the achieved results and speed of neural network training. The achieved results are statistically presented and compared depending on the network topology and changes in the learning algorithm.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/TL02000313" target="_blank" >TL02000313: Intelligent neuro-rehabilitation system for patients with acquired brain damage in early stages of treatment</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

COMPUTERS

ISSN

2073-431X

e-ISSN

—

Volume of the periodical

—

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

28

Pages from-to

701-728

UT code for WoS article

000802477800001

EID of the result in the Scopus database

2-s2.0-85129966490