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Self-learning Genetic Algorithm for Neural Network Topology Optimization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F15%3A%230003693" target="_blank" >RIV/47813059:19520/15:#0003693 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-319-19728-9_15" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19728-9_15</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-19728-9_15" target="_blank" >10.1007/978-3-319-19728-9_15</a>

Alternative languages

  • Result language

    čeština

  • Original language name

    Self-learning Genetic Algorithm for Neural Network Topology Optimization

  • Original language description

    The aim of this paper is presentation of encoding for self-adaptation of genetic algorithms which is suitable for neural network topology optimization. Comparing to previous approaches there is designed the encoding for self-adaptation not only one parameter or several ones but for all possible parameters of genetic algorithms at the same time. The proposed self-learning genetic algorithm is compared with a standard genetic algorithm. The main advantage of this approach is that it makes possible to solve wide range of optimization problems without setting parameters for each type of problem in advance.

  • Czech name

    Self-learning Genetic Algorithm for Neural Network Topology Optimization

  • Czech description

    The aim of this paper is presentation of encoding for self-adaptation of genetic algorithms which is suitable for neural network topology optimization. Comparing to previous approaches there is designed the encoding for self-adaptation not only one parameter or several ones but for all possible parameters of genetic algorithms at the same time. The proposed self-learning genetic algorithm is compared with a standard genetic algorithm. The main advantage of this approach is that it makes possible to solve wide range of optimization problems without setting parameters for each type of problem in advance.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2015

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Smart Innovation Systems and Technologies. Agent and Multi-Agent Systems: Technologies and Applications

  • ISBN

    978-3-319-19728-9

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    179-188

  • Publisher name

    Springer International Publishing

  • Place of publication

    Heidelberg

  • Event location

    Sorrento

  • Event date

    Jun 17, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000359295700015