Comparing Sampling Strategies for the Classification of Bi-objective Problems by FLACCO Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253447" target="_blank" >RIV/61989100:27240/23:10253447 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-35734-3_13" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-35734-3_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-35734-3_13" target="_blank" >10.1007/978-3-031-35734-3_13</a>
Alternative languages
Result language
angličtina
Original language name
Comparing Sampling Strategies for the Classification of Bi-objective Problems by FLACCO Features
Original language description
Problem understanding and the ability to assign problems to distinct classes can improve the usability of metaheuristics. A popular problem-independent method for the characterization of optimization problems is exploratory landscape analysis (ELA). It consists of a sequence of operations that describe the hypersurfaces formed by fitness and other characteristic properties of the problem solutions on the basis of a limited number of samples. Sampling is the initial step of ELA that selects a limited number of candidate solutions for which are the characteristic properties evaluated. The solutions and the computed properties serve as the main inputs for the rest of ELA. In this work, we study the impact of different sampling strategies on machine learning-based classification of bi-objective problems on the basis of FLACCO features. A series of computational experiments demonstrates that different sampling strategies affect the value of the resulting landscape features, their suitability for problem classification, and the overhead of the sampling process. An in-depth analysis of the results also shows the relationship between classification accuracy and the structure of the training data set.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
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
Book/collection name
Lecture Notes on Data Engineering and Communications Technologies. Volume 176
ISBN
978-3-031-35733-6
Number of pages of the result
13
Pages from-to
124-136
Number of pages of the book
408
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
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