On the influence of different randomization and complex network analysis for differential evolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F16%3A43875585" target="_blank" >RIV/70883521:28140/16:43875585 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7744213/" target="_blank" >http://ieeexplore.ieee.org/document/7744213/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC.2016.7744213" target="_blank" >10.1109/CEC.2016.7744213</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the influence of different randomization and complex network analysis for differential evolution

Popis výsledku v původním jazyce

This research deals with the hybridization of the chaos driven heuristics concept and complex networks framework for evolutionary algorithms. This paper aims on the experimental investigations on the influence of different randomization types for chaos-driven Differential Evolution (DE) through the analysis of complex network as a record of population dynamics. The population is visualized as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, clustering coefficient gives diversity of population whereas other attributes like network density, average number of neighbors within the population shows efficiency of the network. Experiments were performed for two different DE strategies, four different randomization types and two simple test functions.

Název v anglickém jazyce

On the influence of different randomization and complex network analysis for differential evolution

Popis výsledku anglicky

This research deals with the hybridization of the chaos driven heuristics concept and complex networks framework for evolutionary algorithms. This paper aims on the experimental investigations on the influence of different randomization types for chaos-driven Differential Evolution (DE) through the analysis of complex network as a record of population dynamics. The population is visualized as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, clustering coefficient gives diversity of population whereas other attributes like network density, average number of neighbors within the population shows efficiency of the network. Experiments were performed for two different DE strategies, four different randomization types and two simple test functions.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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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

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

3346-3353

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Piscataway, New Jersey

Místo konání akce

Vancouver

Datum konání akce

24. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390749103069