Restarted Local Search Algorithms for Continuous Black Box Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00199146" target="_blank" >RIV/68407700:21230/12:00199146 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00087" target="_blank" >http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00087</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Restarted Local Search Algorithms for Continuous Black Box Optimization

Popis výsledku v původním jazyce

Several local search algorithms for real valued domains (axis parallel line search, Nelder Mead simplex search, Rosenbrock's algorithm, quasi Newton method, NEWUOA, and VXQR) are described and thoroughly compared in this article, embedding them in a multi start method. Their comparison aims (1) to help the researchers from the evolutionary community to choose the right opponent for their algorithm (to choose an opponent that would constitute a hard to beat baseline algorithm), (2) to describe individualfeatures of these algorithms and show how they influence the algorithm on different problems, and (3) to provide inspiration for the hybridization of evolutionary algorithms with these local optimizers. The recently proposed Comparing Continuous Optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that in low dimensional spaces, the old method of Nelder and Mead is still the most successful among those compared, while in spaces of higher dimensions

Název v anglickém jazyce

Restarted Local Search Algorithms for Continuous Black Box Optimization

Popis výsledku anglicky

Several local search algorithms for real valued domains (axis parallel line search, Nelder Mead simplex search, Rosenbrock's algorithm, quasi Newton method, NEWUOA, and VXQR) are described and thoroughly compared in this article, embedding them in a multi start method. Their comparison aims (1) to help the researchers from the evolutionary community to choose the right opponent for their algorithm (to choose an opponent that would constitute a hard to beat baseline algorithm), (2) to describe individualfeatures of these algorithms and show how they influence the algorithm on different problems, and (3) to provide inspiration for the hybridization of evolutionary algorithms with these local optimizers. The recently proposed Comparing Continuous Optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that in low dimensional spaces, the old method of Nelder and Mead is still the most successful among those compared, while in spaces of higher dimensions

Druh

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

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GP102%2F08%2FP094" target="_blank" >GP102/08/P094: Metody strojového učení pro konstrukci řešení v evolučních algoritmech</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Evolutionary Computation

ISSN

1063-6560

e-ISSN

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Svazek periodika

20

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

33

Strana od-do

575-607

Kód UT WoS článku

000311334400005

EID výsledku v databázi Scopus

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