Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00645692" target="_blank" >RIV/67985807:_____/25:00645692 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3712255.3734236" target="_blank" >https://doi.org/10.1145/3712255.3734236</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3712255.3734236" target="_blank" >10.1145/3712255.3734236</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context

Popis výsledku v původním jazyce

This paper, originally published in Evolutionary Computation Journal [11], investigates the interplay between surrogate model performance, model settings, and black-box landscape features within the context of Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Our focus is on understanding how landscape characteristics influence surrogate model accuracy during evolutionary optimization, aiming to inform the automated selection and tuning of surrogate models. We perform a comprehensive feature analysis, identifying robust and informative landscape features relevant to surrogate modeling. The analysis explores the error dependencies of four models across 39 settings, utilizing three methods for input data selection, drawn from surrogate-assisted CMA-ES runs on noiseless benchmarks within the Comparing Continuous Optimizers framework. The insights gained can help in the development of adaptive surrogate modeling strategies and metalearning approaches for evolutionary computation.

Název v anglickém jazyce

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context

Popis výsledku anglicky

This paper, originally published in Evolutionary Computation Journal [11], investigates the interplay between surrogate model performance, model settings, and black-box landscape features within the context of Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Our focus is on understanding how landscape characteristics influence surrogate model accuracy during evolutionary optimization, aiming to inform the automated selection and tuning of surrogate models. We perform a comprehensive feature analysis, identifying robust and informative landscape features relevant to surrogate modeling. The analysis explores the error dependencies of four models across 39 settings, utilizing three methods for input data selection, drawn from surrogate-assisted CMA-ES runs on noiseless benchmarks within the Comparing Continuous Optimizers framework. The insights gained can help in the development of adaptive surrogate modeling strategies and metalearning approaches for evolutionary computation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

GECCO '25 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

ISBN

979-8-4007-1464-1

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

51-52

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Málaga / hybrid

Datum konání akce

14. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001564494900026