Fitness Landscape k-Nearest Neighbors Classification Based on Fitness Values Distribution

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257021" target="_blank" >RIV/61989100:27240/24:10257021 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10611886" target="_blank" >https://ieeexplore.ieee.org/document/10611886</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC60901.2024.10611886" target="_blank" >10.1109/CEC60901.2024.10611886</a>

Result language
angličtina
Original language name
Fitness Landscape k-Nearest Neighbors Classification Based on Fitness Values Distribution
Original language description
Metaheuristic algorithms prove efficient in addressing complex optimization problems. Selecting a suitable algorithm for a specific problem type can improve optimization per-formance that can vary across different problem types. A problem can be characterized by its Fitness Landscape (FL). Exploratory Landscape Analysis approximates the problem's FL by features computed from only a small number of random samples. In this work, we investigate FL representation by a normalized histogram of samples' fitness values. The histograms are used as simple problem representations for a k-Nearest Neighbors classification to examine their ability to represent the problems. We provide a comprehensive study of classification performance on 24 single-objective benchmark problems. Especially, the impact of different sampling strategies, distance measures, and numbers of histogram bins on classification accuracy for different problems is examined. The results support the usefulness of this representation and overall approach and reveal some interesting trends.
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2024 IEEE Congress on Evolutionary Computation, CEC 2024 : proceedings
ISBN
979-8-3503-0837-2
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
1-9
Publisher name
IEEE
Place of publication
Piscataway
Event location
Jokohama
Event date
Jun 30, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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