Suitability of Modern Neural Networks for Active and Transfer Learning in Surrogate-Assisted Black-Box Optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00600285" target="_blank" >RIV/67985807:_____/24:00600285 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/24:00377328

Result on the web

<a href="https://www.activeml.net/ial2024/pdf/paper6.pdf" target="_blank" >https://www.activeml.net/ial2024/pdf/paper6.pdf</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Suitability of Modern Neural Networks for Active and Transfer Learning in Surrogate-Assisted Black-Box Optimization

Original language description

Active learning plays a crucial role in black-box optimization, especially for objective functions that are expensive to evaluate. Continuous black-box optimization has adopted an approach called surrogate modelling, where the original black-box objective is approximated with a regression model. An active learning task in this context is to decide which points should be evaluated using the original objective to update the surrogate model. Apart from low-order polynomials, the first surrogate models were artificial neural networks of the kinds multilayer perceptron and radial basis function network. In the late 2000s, neural networks have been superseded by other kinds of surrogate models, primarily Gaussian processes. However, over the last 15 years, neural networks have seen significant and successful development, suggesting that they once again have the potential to serve as promising surrogate models. This paper reviews possible research directions concerning that potential, and recalls initial results from investigations in some of these directions. Finally, it contributes to those results by investigating the state-of-the-art black-box optimizer CMA-ES surrogate-assisted by two variants of random-activation-function neural network ensembles.

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

2024

Confidentiality

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

Article name in the collection

Interactive Adaptive Learning 2024: Proceedings of the Workshop on Interactive Adaptive Learning

ISBN

—

ISSN

1613-0073

e-ISSN

—

Number of pages

21

Pages from-to

47-67

Publisher name

Technical University & CreateSpace Independent Publishing

Place of publication

Aachen

Event location

Vilnius

Event date

Sep 9, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—