Sampling Strategies for Exploratory Landscape Analysis of Bi-Objective Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10253457" target="_blank" >RIV/61989100:27240/22:10253457 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10216481" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10216481</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CSCI58124.2022.00067" target="_blank" >10.1109/CSCI58124.2022.00067</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sampling Strategies for Exploratory Landscape Analysis of Bi-Objective Problems

Popis výsledku v původním jazyce

Exploratory landscape analysis (ELA) is a popular method for the understanding of complex, often black-box optimization problems. It tries to approximate and describe the surfaces formed by the fitness and other characteristic values associated with problem solutions on top of the multi-dimensional solution spaces. Sampling is the initial step of the ELA pipeline. It is a strategy for selecting a limited number of solutions, i.e., points in the multi-dimensional solution space, for which the fitness function(s) are evaluated. Consequently, the fitness landscape is approximated and its properties are drawn from these fitness values. In this work, the properties and the impact of various sampling strategies on the analysis of the fitness landscape are studied in the context of bi-objective optimization. Extensive computational experiments show that the use of different sampling strategies affects both the value of high-level landscape features and their usability for problem classification. The results also demonstrate that the magnitude and significance of the impact depend on problem dimension and sample size.

Název v anglickém jazyce

Sampling Strategies for Exploratory Landscape Analysis of Bi-Objective Problems

Popis výsledku anglicky

Exploratory landscape analysis (ELA) is a popular method for the understanding of complex, often black-box optimization problems. It tries to approximate and describe the surfaces formed by the fitness and other characteristic values associated with problem solutions on top of the multi-dimensional solution spaces. Sampling is the initial step of the ELA pipeline. It is a strategy for selecting a limited number of solutions, i.e., points in the multi-dimensional solution space, for which the fitness function(s) are evaluated. Consequently, the fitness landscape is approximated and its properties are drawn from these fitness values. In this work, the properties and the impact of various sampling strategies on the analysis of the fitness landscape are studied in the context of bi-objective optimization. Extensive computational experiments show that the use of different sampling strategies affects both the value of high-level landscape features and their usability for problem classification. The results also demonstrate that the magnitude and significance of the impact depend on problem dimension and sample size.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GF22-34873K" target="_blank" >GF22-34873K: Vícekriteriální optimalizace s omezeními pomocí analýzy potenciálních ploch</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022 : proceedings

ISBN

979-8-3503-2029-9

ISSN

2769-5670

e-ISSN

2769-5654

Počet stran výsledku

7

Strana od-do

336-342

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Las Vegas

Datum konání akce

14. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—