Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F18%3A00326672" target="_blank" >RIV/68407700:21340/18:00326672 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2203/72.pdf" target="_blank" >http://ceur-ws.org/Vol-2203/72.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy

Popis výsledku v původním jazyce

Many real-world problems belong to the area of continuous black-box optimization, where evolutionary optimizers have become very popular inspite of the fact that such optimizers require a great amount of real-world fitness function evaluations, which can be very expensive or time-consuming. Hence, regression surrogate models are often utilized to evaluate some points instead of the fitness function. The Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy (DTS-CMA-ES) is a surrogate-assisted version of the state-of-the-art continuous black-box optimizer CMA-ES using Gausssian processes as a surrogate model to predict the whole distribution of the fitness function. In this paper, the DTS-CMA-ES is studied in connection with the boosted regression forest, another regression model capable to estimate the distribution. Results of testing regression forest and Gaussian processes, the former in 20 different settings, as a surrogate models in the DTS-CMA-ES on the set of noiseless benchmarks are reported.

Název v anglickém jazyce

Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy

Popis výsledku anglicky

Many real-world problems belong to the area of continuous black-box optimization, where evolutionary optimizers have become very popular inspite of the fact that such optimizers require a great amount of real-world fitness function evaluations, which can be very expensive or time-consuming. Hence, regression surrogate models are often utilized to evaluate some points instead of the fitness function. The Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy (DTS-CMA-ES) is a surrogate-assisted version of the state-of-the-art continuous black-box optimizer CMA-ES using Gausssian processes as a surrogate model to predict the whole distribution of the fitness function. In this paper, the DTS-CMA-ES is studied in connection with the boosted regression forest, another regression model capable to estimate the distribution. Results of testing regression forest and Gaussian processes, the former in 20 different settings, as a surrogate models in the DTS-CMA-ES on the set of noiseless benchmarks are reported.

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 18th Conference Information Technologies - Applications and Theory (ITAT 2018)

ISBN

9781727267198

ISSN

—

e-ISSN

1613-0073

Počet stran výsledku

8

Strana od-do

72-79

Název nakladatele

CEUR Workshop Proceedings

Místo vydání

Aachen

Místo konání akce

Krompachy

Datum konání akce

21. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—