On the Evolutionary Neural Network Creation Using Native Visibility Graph

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10244014" target="_blank" >RIV/61989100:27240/19:10244014 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/8789941" target="_blank" >https://ieeexplore.ieee.org/abstract/document/8789941</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC.2019.8789941" target="_blank" >10.1109/CEC.2019.8789941</a>

Result language
angličtina
Original language name
On the Evolutionary Neural Network Creation Using Native Visibility Graph
Original language description
In this article, a novel way of creating the feed forward neural network is presented. This model is inspired by native visibility graph, and the networks are created by well-known differential evolution algorithm. In the experiment parts, we compared these networks with multi-layer perceptron. We have found that our novel approach creates smaller networks comparing to multi-layer perceptron and also, the decision space differs. The decision space was similar for multi-layer perceptron for almost all training runs from 51 repetitions, but for our novel approach, the decision space differs and also creates more complex patterns.
Czech name
—
Czech description
—

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
ISBN
978-1-72812-153-6
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
1494-1501
Publisher name
IEEE
Place of publication
Piscataway
Event location
Wellington
Event date
Jun 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000502087101069

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