Small-world hidden in differential evolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86100212" target="_blank" >RIV/61989100:27240/16:86100212 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CEC.2016.7744214" target="_blank" >http://dx.doi.org/10.1109/CEC.2016.7744214</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC.2016.7744214" target="_blank" >10.1109/CEC.2016.7744214</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Small-world hidden in differential evolution

Popis výsledku v původním jazyce

Differential evolution is an effective population-based global optimizer which is used in many areas of research. The population consists of individuals, which are mutated, crossed and better of them survive to the next generation. In this paper, we look at this process as at the communication between individuals which can be modeled by the network where the individuals are represented by the nodes and the edges between them reflect the dynamics in the population, i.e. interactions between individuals. The main goal of this work is to find out if the differential evolution algorithm is able to create the networks where the small-world phenomenon (known as six degrees of separation) is observed. The secondary objective was to investigate the dependency between the type of the selected test function and the extent of this phenomenon. To evaluate the performance of the algorithm eleven test functions from the benchmark set CEC 2015 have been used. The analysis of the generated networks indicates that the differential evolution is able to create small-world networks in majority of test functions. As the result, the selected test functions can be classified into three categories which binds to the degree of cooperation between the individuals in the population

Název v anglickém jazyce

Small-world hidden in differential evolution

Popis výsledku anglicky

Differential evolution is an effective population-based global optimizer which is used in many areas of research. The population consists of individuals, which are mutated, crossed and better of them survive to the next generation. In this paper, we look at this process as at the communication between individuals which can be modeled by the network where the individuals are represented by the nodes and the edges between them reflect the dynamics in the population, i.e. interactions between individuals. The main goal of this work is to find out if the differential evolution algorithm is able to create the networks where the small-world phenomenon (known as six degrees of separation) is observed. The secondary objective was to investigate the dependency between the type of the selected test function and the extent of this phenomenon. To evaluate the performance of the algorithm eleven test functions from the benchmark set CEC 2015 have been used. The analysis of the generated networks indicates that the differential evolution is able to create small-world networks in majority of test functions. As the result, the selected test functions can be classified into three categories which binds to the degree of cooperation between the individuals in the population

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GA15-06700S" target="_blank" >GA15-06700S: Nekonvenční řízení komplexních systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

2016 IEEE Congress on Evolutionary Computation, CEC 2016

ISBN

978-1-5090-0622-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

3354-3361

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Vancouver

Datum konání akce

24. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390749103070