Fitness Histograms of Expert-Defined Problem Classes in Fitness Landscape Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257022" target="_blank" >RIV/61989100:27240/24:10257022 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scitepress.org/Documents/2024/129239/" target="_blank" >https://www.scitepress.org/Documents/2024/129239/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0012923900003837" target="_blank" >10.5220/0012923900003837</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fitness Histograms of Expert-Defined Problem Classes in Fitness Landscape Classification

Popis výsledku v původním jazyce

Various metaheuristic algorithms can be employed to find optimal or sub-optimal solutions for different problems. A fitness landscape (FL) is an abstraction representing a specific optimization task. Exploratory landscape analysis (ELA) approximates the FL by estimating its features from a limited number of random solution samples. Such ELA features help in estimating the properties of the FL and ultimately aid the selection of suitable optimization algorithms for problems with certain FL characteristics. This paper proposes using a normalized histogram of fitness values as a simple statistical feature vector for representing FLs. These histograms are classified using various classifiers to evaluate their effectiveness in representing different problems. The study focuses on 24 single-objective benchmark problems, grouped into five expert-defined classes. The performance of several classifiers is compared across different problem dimensions and sample sizes, emphasizing the impact of different sampling strategies and the number of histogram bins. The findings highlight the robustness of histogram representation and reveal promising experimental setups and relationships.

Název v anglickém jazyce

Fitness Histograms of Expert-Defined Problem Classes in Fitness Landscape Classification

Popis výsledku anglicky

Various metaheuristic algorithms can be employed to find optimal or sub-optimal solutions for different problems. A fitness landscape (FL) is an abstraction representing a specific optimization task. Exploratory landscape analysis (ELA) approximates the FL by estimating its features from a limited number of random solution samples. Such ELA features help in estimating the properties of the FL and ultimately aid the selection of suitable optimization algorithms for problems with certain FL characteristics. This paper proposes using a normalized histogram of fitness values as a simple statistical feature vector for representing FLs. These histograms are classified using various classifiers to evaluate their effectiveness in representing different problems. The study focuses on 24 single-objective benchmark problems, grouped into five expert-defined classes. The performance of several classifiers is compared across different problem dimensions and sample sizes, emphasizing the impact of different sampling strategies and the number of histogram bins. The findings highlight the robustness of histogram representation and reveal promising experimental setups and relationships.

Druh

D - Stať ve sborníku

CEP obor

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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

Proceedings of the 16th International Joint Conference on Computational Intelligence

ISBN

978-989-758-721-4

ISSN

2184-3236

e-ISSN

—

Počet stran výsledku

9

Strana od-do

205-213

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

Setúbal

Místo konání akce

Porto

Datum konání akce

20. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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