Abstract
Various optimization methods and parameters of feedforward neural networks by evolutionary algorithms are considered. The analysis of neuroevolutionary methods, basic properties and applicability of various variants of evolutionary optimization with direct coding of chromosomes for various architectures of feedforward neural networks are presented. Operators of the genetic algorithm, advantages, disadvantages and features of their application are described for each method of neuroevolution. The comparison results are presented in tabular form.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Vestnik of Brest State Technical University. Civil Engineering and Architecture
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
