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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;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&apos; 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;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&apos; 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.

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 IEEE Congress on Evolutionary Computation, CEC 2024 : proceedings

ISBN

979-8-3503-0837-2

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

1-9

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Jokohama

Datum konání akce

30. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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