Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00317541" target="_blank" >RIV/68407700:21340/17:00317541 - isvavai.cz</a>

Alternative codes found

RIV/00023752:_____/17:43919251

Result on the web

—

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy

Original language description

An area of increasingly frequent applications of evolutionary optimization to real-world problems is continuous black-box optimization. However, evaluating real-world black-box fitness functions is sometimes very time-consuming or expensive, which interferes with the need of evolutionary algorithms for many fitness evaluations. Therefore, surrogate regression models replacing the original expensive fitness in some of the evaluated points have been in use since the early 2000s. The Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy (DTS-CMA-ES) represents a surrogate-assisted version of the state-of-the-art algorithm for continuous black- box optimization CMA-ES. The DTS-CMA-ES saves expensive function evaluations through using a surrogate model. However, the model inaccuracy on some functions can slow-down the algorithm convergence. This paper investigates an extension of DTS-CMA-ES which controls the usage of the model according to the model’s error. Results of testing an adaptive and the original version of 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/LO1611" target="_blank" >LO1611: Sustainability for The National Institute of Mental Health</a><br>

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

ITAT 2017: Information Technologies – Applications and Theory

ISBN

—

ISSN

1613-0073

e-ISSN

1613-0073

Number of pages

9

Pages from-to

120-128

Publisher name

CEUR Workshop Proceedings

Place of publication

Aachen

Event location

Martinské hole, Malá Fatra

Event date

Sep 22, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—