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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AA2302H1D" target="_blank" >RIV/61988987:17310/22:A2302H1D - isvavai.cz</a>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/TL02000313" target="_blank" >TL02000313: Chytrý neuro-rehabilitační systém pro pacienty se získaným poškozením mozku v časných stádiích léčby</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

COMPUTERS

ISSN

2073-431X

e-ISSN

—

Svazek periodika

—

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

28

Strana od-do

701-728

Kód UT WoS článku

000802477800001

EID výsledku v databázi Scopus

2-s2.0-85129966490