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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Article name in the collection

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

ISBN

979-8-4007-1464-1

ISSN

—

e-ISSN

—

Number of pages

2

Pages from-to

51-52

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Málaga / hybrid

Event date

Jul 14, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001564494900026