All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Multi-agent architecture for Multi-objective optimization of Flexible Neural Tree

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099095" target="_blank" >RIV/61989100:27240/16:86099095 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1016/j.neucom.2016.06.019" target="_blank" >http://dx.doi.org/10.1016/j.neucom.2016.06.019</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.neucom.2016.06.019" target="_blank" >10.1016/j.neucom.2016.06.019</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Multi-agent architecture for Multi-objective optimization of Flexible Neural Tree

  • Original language description

    In this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)' training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning process were the accuracy and the structure complexity. With the proposed multi-agent system, the main purpose is to reach a good balance between these objectives. For that, a multi-objective context was adopted which based on Pareto dominance. The agents use two algorithms: the Pareto dominance Extended Genetic Programming (PEGP) and the Pareto Multi-Dimensional Particle Swarm Optimization (PMD_PSO) algorithms for the structure and parameter optimization, respectively. The proposed system is called Pareto Multi-Agent Flexible Neural Tree (PMA_FNT).To assess the effectiveness of . PMA_FNT, four benchmark real datasets of classification are tested. The results compared with some classifiers published in the literature. (C) 2016 Elsevier B.V.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neurocomputing

  • ISSN

    0925-2312

  • e-ISSN

  • Volume of the periodical

    214

  • Issue of the periodical within the volume

    NOV

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    10

  • Pages from-to

    307-316

  • UT code for WoS article

    000386741300029

  • EID of the result in the Scopus database

    2-s2.0-84978763693