Evaluation of differential evolution with interaction network on a real-parameter optimization benchmark

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F16%3A86100042" target="_blank" >RIV/61989100:27740/16:86100042 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/16:86100042

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7744165/" target="_blank" >http://ieeexplore.ieee.org/document/7744165/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC.2016.7744165" target="_blank" >10.1109/CEC.2016.7744165</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of differential evolution with interaction network on a real-parameter optimization benchmark

Popis výsledku v původním jazyce

Differential evolution (DE) is a popular member of the wide family of population-based evolutionary optimization methods. These general purpose methods solve arbitrary problems by iteratively evolving a pool (population) of candidate solutions. Candidate solutions are during the artificial evolution updated and modified so that they efficiently explore solution space of the solved problem. Population-based metaheuristics focus on finding local or global optima with respect to selected optimization criteria (objective function). The iterative updates of candidate solutions usually involve some sort of interaction and information exchange. This behaviour has been recently cast as a temporal interaction network to allow a network-centric representation and research of artificial evolution. In this paper, we use a network-based model of the interactions in DE to improve the underlying algorithm. A simple extension of a traditional DE utilizing the properties of its interaction network is proposed in order to study the usefulness of this concept. The extended algorithm is evaluated on the CEC 2016 real-parameter optimization benchmark. (C) 2016 IEEE.

Název v anglickém jazyce

Evaluation of differential evolution with interaction network on a real-parameter optimization benchmark

Popis výsledku anglicky

Differential evolution (DE) is a popular member of the wide family of population-based evolutionary optimization methods. These general purpose methods solve arbitrary problems by iteratively evolving a pool (population) of candidate solutions. Candidate solutions are during the artificial evolution updated and modified so that they efficiently explore solution space of the solved problem. Population-based metaheuristics focus on finding local or global optima with respect to selected optimization criteria (objective function). The iterative updates of candidate solutions usually involve some sort of interaction and information exchange. This behaviour has been recently cast as a temporal interaction network to allow a network-centric representation and research of artificial evolution. In this paper, we use a network-based model of the interactions in DE to improve the underlying algorithm. A simple extension of a traditional DE utilizing the properties of its interaction network is proposed in order to study the usefulness of this concept. The extended algorithm is evaluated on the CEC 2016 real-parameter optimization benchmark. (C) 2016 IEEE.

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

2974-2981

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

—