Global Line Search Algorithm Hybridized with Quadratic Interpolation and Its Extension to Separable Functions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00236360" target="_blank" >RIV/68407700:21230/15:00236360 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/2739480.2754717" target="_blank" >http://dx.doi.org/10.1145/2739480.2754717</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2739480.2754717" target="_blank" >10.1145/2739480.2754717</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Global Line Search Algorithm Hybridized with Quadratic Interpolation and Its Extension to Separable Functions

Popis výsledku v původním jazyce

We propose a novel hybrid algorithm "Brent-STEP" for univariate global function minimization, based on the global line search method STEP and accelerated by Brent's method, a local optimizer that combines quadratic interpolation and golden section steps.We analyze the performance of the hybrid algorithm on various one-dimensional functions and experimentally demonstrate a significant improvement relative to its constituent algorithms in most cases. We then generalize the algorithm to multivariate functions, proposing a scheme to interleave evaluations across dimensions to achieve smoother and more efficient convergence. We experimentally demonstrate the highly competitive performance of the proposed multivariate algorithm on separable functions of theBBOB benchmark. The combination of good performance and smooth convergence on separable functions makes the algorithm an interesting candidate for inclusion in algorithmic portfolios or hybrid algorithms that aim to provide good performa

Název v anglickém jazyce

Global Line Search Algorithm Hybridized with Quadratic Interpolation and Its Extension to Separable Functions

Popis výsledku anglicky

We propose a novel hybrid algorithm "Brent-STEP" for univariate global function minimization, based on the global line search method STEP and accelerated by Brent's method, a local optimizer that combines quadratic interpolation and golden section steps.We analyze the performance of the hybrid algorithm on various one-dimensional functions and experimentally demonstrate a significant improvement relative to its constituent algorithms in most cases. We then generalize the algorithm to multivariate functions, proposing a scheme to interleave evaluations across dimensions to achieve smoother and more efficient convergence. We experimentally demonstrate the highly competitive performance of the proposed multivariate algorithm on separable functions of theBBOB benchmark. The combination of good performance and smooth convergence on separable functions makes the algorithm an interesting candidate for inclusion in algorithmic portfolios or hybrid algorithms that aim to provide good performa

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

GECCO '15 Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

ISBN

978-1-4503-3472-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

257-264

Název nakladatele

ACM Press

Místo vydání

New York

Místo konání akce

Madrid

Datum konání akce

11. 7. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000358795700033