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%2F67985807%3A_____%2F18%3A00494112" target="_blank" >RIV/67985807:_____/18:00494112 - 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

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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 in spite 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-CMAES 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 in spite 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-CMAES 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

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OECD FORD obor

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

Projekt

<a href="/cs/project/GA17-01251S" target="_blank" >GA17-01251S: Metaučení pro extrakci pravidel s numerickými konsekventy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

72-79

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen

Místo konání akce

Plejsy

Datum konání akce

21. 9. 2018

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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