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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

214

Číslo periodika v rámci svazku

NOV

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

307-316

Kód UT WoS článku

000386741300029

EID výsledku v databázi Scopus

2-s2.0-84978763693