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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

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

2023

Confidentiality

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

Article name in the collection

ACM International Conference Proceeding Series 2023

ISBN

979-8-4007-0849-7

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

1-9

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Bangkok

Event date

Dec 6, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—