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

Result code in 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>

Result on the web

<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

—

Result language

angličtina

Original language name

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

Original language description

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.

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

<a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>

Continuities

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

Publication year

2018

Confidentiality

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

Article name in the collection

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

ISBN

—

ISSN

1613-0073

e-ISSN

—

Number of pages

8

Pages from-to

72-79

Publisher name

Technical University & CreateSpace Independent Publishing Platform

Place of publication

Aachen

Event location

Plejsy

Event date

Sep 21, 2018

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

—