Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00334845" target="_blank" >RIV/68407700:21340/19:00334845 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/19:00508171
Result on the web
<a href="http://dx.doi.org/10.1145/3321707.3321861" target="_blank" >http://dx.doi.org/10.1145/3321707.3321861</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3321707.3321861" target="_blank" >10.1145/3321707.3321861</a>
Alternative languages
Result language
angličtina
Original language name
Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy
Original language description
Gaussian processes modeling technique has been shown as a valuable surrogate model for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in continuous single-objective black-box optimization tasks, where the optimized function is expensive. In this paper, we investigate how different Gaussian process settings influence the error between the predicted and genuine population ordering in connection with features representing the fitness landscape. Apart from using features for landscape analysis known from the literature, we propose a new set of features based on CMA-ES state variables. We perform the landscape analysis of a large set of data generated using runs of a surrogate-assisted version of the CMA-ES on the noiseless part of the Comparing Continuous Optimisers benchmark function testbed.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference
ISBN
978-1-4503-6111-8
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
691-699
Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Praha
Event date
Jul 13, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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