Analysing knowledge transfer in SHADE via complex network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63523498" target="_blank" >RIV/70883521:28140/20:63523498 - isvavai.cz</a>

Výsledek na webu

<a href="https://academic.oup.com/jigpal/article/28/2/153/5107008?login=true" target="_blank" >https://academic.oup.com/jigpal/article/28/2/153/5107008?login=true</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/jigpal/jzy042" target="_blank" >10.1093/jigpal/jzy042</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysing knowledge transfer in SHADE via complex network

Popis výsledku v původním jazyce

In this research paper a hybridization of two computational intelligence fields, which are evolutionary computation techniques and complex networks (CNs), is presented. During the optimization run of the success-history based adaptive differential evolution (SHADE) a CN is built and its feature, node degree centrality, is extracted for each node. Nodes represent here the individual solutions from the SHADE population. Edges in the network mirror the knowledge transfer between individuals in SHADE’s population, and therefore, the node degree centrality can be used to measure knowledge transfer capabilities of each individual. The correlation between individual’s quality and its knowledge transfer capability is recorded and analyzed on the CEC2015 benchmark set in three different dimensionality settings—10D, 30D and 50D. Results of the analysis are discussed, and possible directions for future research are suggested.

Název v anglickém jazyce

Analysing knowledge transfer in SHADE via complex network

Popis výsledku anglicky

In this research paper a hybridization of two computational intelligence fields, which are evolutionary computation techniques and complex networks (CNs), is presented. During the optimization run of the success-history based adaptive differential evolution (SHADE) a CN is built and its feature, node degree centrality, is extracted for each node. Nodes represent here the individual solutions from the SHADE population. Edges in the network mirror the knowledge transfer between individuals in SHADE’s population, and therefore, the node degree centrality can be used to measure knowledge transfer capabilities of each individual. The correlation between individual’s quality and its knowledge transfer capability is recorded and analyzed on the CEC2015 benchmark set in three different dimensionality settings—10D, 30D and 50D. Results of the analysis are discussed, and possible directions for future research are suggested.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

LOGIC JOURNAL OF THE IGPL

ISSN

1367-0751

e-ISSN

—

Svazek periodika

28

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

153-170

Kód UT WoS článku

000559006600002

EID výsledku v databázi Scopus

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