Battle royale optimizer for training multi-layer perceptron

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F22%3A39921585" target="_blank" >RIV/00216275:25530/22:39921585 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s12530-021-09401-5" target="_blank" >https://link.springer.com/article/10.1007/s12530-021-09401-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12530-021-09401-5" target="_blank" >10.1007/s12530-021-09401-5</a>

Result language

angličtina

Original language name

Battle royale optimizer for training multi-layer perceptron

Original language description

Artificial neural network (ANN) is one of the most successful tools in machine learning. The success of ANN mostly depends on its architecture and learning procedure. Multi-layer perceptron (MLP) is a popular form of ANN. Moreover, backpropagation is a well-known gradient-based approach for training MLP. Gradient-based search approaches have a low convergence rate; therefore, they may get stuck in local minima, which may lead to performance degradation. Training the MLP is accomplished based on minimizing the total network error, which can be considered as an optimization problem. Stochastic optimization algorithms are proven to be effective when dealing with such problems. Battle royale optimization (BRO) is a recently proposed population-based metaheuristic algorithm which can be applied to single-objective optimization over continuous problem spaces. The proposed method has been compared with backpropagation (Generalized learning delta rule) and six well-known optimization algorithms on ten classification benchmark datasets. Experiments confirm that, according to error rate, accuracy, and convergence, the proposed approach yields promising results and outperforms its competitors.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Evolving Systems

ISSN

1868-6478

e-ISSN

1868-6486

Volume of the periodical

13

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

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UT code for WoS article

000686998900001

EID of the result in the Scopus database

2-s2.0-85113189557