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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Fitness Histograms of Expert-Defined Problem Classes in Fitness Landscape Classification
Original language description
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.
Czech name
—
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
Proceedings of the 16th International Joint Conference on Computational Intelligence
ISBN
978-989-758-721-4
ISSN
2184-3236
e-ISSN
—
Number of pages
9
Pages from-to
205-213
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Setúbal
Event location
Porto
Event date
Nov 20, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

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