Randomization of Low-discrepancy Sampling Designs by Cranley-Patterson Rotation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253619" target="_blank" >RIV/61989100:27240/23:10253619 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/10.1145/3628454.3631564" target="_blank" >https://dl.acm.org/doi/10.1145/3628454.3631564</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3628454.3631564" target="_blank" >10.1145/3628454.3631564</a>
Alternative languages
Result language
angličtina
Original language name
Randomization of Low-discrepancy Sampling Designs by Cranley-Patterson Rotation
Original language description
Complex problems are often addressed by methods from the domain of computational intelligence, including metaheuristic algorithms. Different metaheuristics have different abilities to solve specific types of problems and the selection of suitable methods has a large impact on the ability to find good problem solutions. Problem characterization became an important step in the application of intelligent methods to practical problems. A popular approach to problem characterization is the exploratory landscape analysis. It consists of a sequence of operations that approximate and describe the hypersurfaces formed by characteristic problem properties from a limited sample of solutions. Exploratory landscape analysis uses a particular strategy to select just a small subset of problem solutions for which are the characteristic properties evaluated and high-level landscape features computed. Low-discrepancy sequences have been recently used to design a family of sampling strategies. They have useful space-filling properties but their effective and efficient randomization might represent an issue. In this work, we study the Cranley-Patterson rotation, a lightweight randomization strategy for low-discrepancy sequences, compare it with other randomization methods, and observe the effect its use has on the randomization of sets of sampling points in the context of exploratory landscape analysis.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
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)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ACM International Conference Proceeding Series 2023
ISBN
979-8-4007-0849-7
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Bangkok
Event date
Dec 6, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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