COCOpf: An Algorithm Portfolio Framework

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00217792" target="_blank" >RIV/68407700:21230/14:00217792 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

COCOpf: An Algorithm Portfolio Framework

Popis výsledku v původním jazyce

Algorithm portfolios represent a strategy of composing multiple heuristic algorithms, each suited to a different class of problems, within a single general solver that will choose the best suited algorithm for each input. This approach recently gained popularity especially for solving combinatoric problems, but optimization applications are still emerging. The COCO platform of the BBOB workshop series is the current standard way to measure performance of continuous black-box optimization algorithms. Asan extension to the COCO platform, we present the Python-based COCOpf framework that allows composing portfolios of optimization algorithms and running experiments with different selection strategies. In our framework, we focus on black-box algorithm portfolio and online adaptive selection. As a demonstration, we measure the performance of stock SciPy optimization algorithms and the popular CMA algorithm alone and in a portfolio with two simple selection strategies. We confirm that even

Název v anglickém jazyce

COCOpf: An Algorithm Portfolio Framework

Popis výsledku anglicky

Algorithm portfolios represent a strategy of composing multiple heuristic algorithms, each suited to a different class of problems, within a single general solver that will choose the best suited algorithm for each input. This approach recently gained popularity especially for solving combinatoric problems, but optimization applications are still emerging. The COCO platform of the BBOB workshop series is the current standard way to measure performance of continuous black-box optimization algorithms. Asan extension to the COCO platform, we present the Python-based COCOpf framework that allows composing portfolios of optimization algorithms and running experiments with different selection strategies. In our framework, we focus on black-box algorithm portfolio and online adaptive selection. As a demonstration, we measure the performance of stock SciPy optimization algorithms and the popular CMA algorithm alone and in a portfolio with two simple selection strategies. We confirm that even

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

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

POSTER 2014 - 18th International Student Conference on Electrical Engineering

ISBN

978-80-01-05499-4

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

Czech Technical University

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

15. 5. 2014

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—