Classification with Pseudo Neural Networks Based On Evolutionary Symbolic Regression
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F11%3A43866764" target="_blank" >RIV/70883521:28140/11:43866764 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Classification with Pseudo Neural Networks Based On Evolutionary Symbolic Regression
Original language description
This research deals with a novel approach to classification. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, was an inspiration for this work. Artificial neural networks need to optimize weights, but the structure and transfer functions are usually set up before the training. There exist some evolutionary approaches, which help to set up the structure or to optimize weights in different ways than standard artificial neural networks do. The proposed method utilizes the symbolic regression for synthesis of a whole structure, i.e. the relation between inputs and output(s). For experimentation, Differential Evolution (DE) and Self Organizing Migrating Algorithm (SOMA) for the main procedure of analytic programming (AP) and DE as an algorithm for meta-evolution were used.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Compting
ISBN
978-0-7695-4531-8
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
396-401
Publisher name
IEEE Operations Center
Place of publication
Piscataway
Event location
Barcelona, Španělsko
Event date
Oct 26, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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