Sampling Strategies for Exploratory Landscape Analysis of Bi-Objective Problems

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Sampling Strategies for Exploratory Landscape Analysis of Bi-Objective Problems
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GF22-34873K" target="_blank" >GF22-34873K: Constrained Multiobjective Optimization Based on Problem Landscape Analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
Number of pages
7
Pages from-to
336-342
Publisher name
IEEE
Place of publication
Piscataway
Event location
Las Vegas
Event date
Dec 14, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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