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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

  • DOI - Digital Object Identifier

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

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

  • e-ISSN

  • 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