Impact of Different Discrete Sampling Strategies on Fitness Landscape Analysis Based on Histograms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253620" target="_blank" >RIV/61989100:27240/23:10253620 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/abs/10.1145/3628454.3631563" target="_blank" >https://dl.acm.org/doi/abs/10.1145/3628454.3631563</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3628454.3631563" target="_blank" >10.1145/3628454.3631563</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Impact of Different Discrete Sampling Strategies on Fitness Landscape Analysis Based on Histograms

Popis výsledku v původním jazyce

Complex problems are frequently tackled using techniques from the realm of computational intelligence and metaheuristic algorithms. Selection of a metaheuristic from the wide range of algorithms possessing various properties to address specific problem types efficiently is a difficult and crucial task to avoid unnecessary blind alleys and computational expenses. Approximation of continuous problem landscapes by a limited number of scattered discrete samples is a widespread problem characterization applied in exploratory landscape analysis (ELA). ELA is a set of methods analyzing the objective and solution spaces of a problem to construct features estimated from the random samples. This paper describes a simple method for fitness landscape analysis based on the normalized histograms of sample fitnesses. Generation of a small number of representative discrete samples is crucial for efficient problem characterization, and therefore, amount of sampling strategies including random generators and low-discrepancy sequences was developed to evenly cover the problem landscapes. The main contribution of this paper is a study examining the impact of different sampling strategies on the distribution of fitness values based on the normalized histogram analysis. The results reveal a strong effect.

Název v anglickém jazyce

Impact of Different Discrete Sampling Strategies on Fitness Landscape Analysis Based on Histograms

Popis výsledku anglicky

Complex problems are frequently tackled using techniques from the realm of computational intelligence and metaheuristic algorithms. Selection of a metaheuristic from the wide range of algorithms possessing various properties to address specific problem types efficiently is a difficult and crucial task to avoid unnecessary blind alleys and computational expenses. Approximation of continuous problem landscapes by a limited number of scattered discrete samples is a widespread problem characterization applied in exploratory landscape analysis (ELA). ELA is a set of methods analyzing the objective and solution spaces of a problem to construct features estimated from the random samples. This paper describes a simple method for fitness landscape analysis based on the normalized histograms of sample fitnesses. Generation of a small number of representative discrete samples is crucial for efficient problem characterization, and therefore, amount of sampling strategies including random generators and low-discrepancy sequences was developed to evenly cover the problem landscapes. The main contribution of this paper is a study examining the impact of different sampling strategies on the distribution of fitness values based on the normalized histogram analysis. The results reveal a strong effect.

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

ACM International Conference Proceeding Series 2023

ISBN

979-8-4007-0849-7

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

1-9

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Bangkok

Datum konání akce

6. 12. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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