Search results
- 1.0579713 - ÚI 2024 RIV DE eng C - Conference Paper (international conference)
Tumpach, J. - Koza, J. - Holeňa, Martin
Improving Optimization With Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy.
Proceedings of the 23st Conference Information Technologies – Applications and Theory (ITAT 2023). Aachen: Technical University & CreateSpace Independent Publishing, 2023 - (Brejová, B.; Ciencialová, L.; Holeňa, M.; Jajcay, R.; Jajcayová, T.; Lexa, M.; Mráz, F.; Pardubská, D.; Plátek, M.), s. 82-88. CEUR Workshop Proceedings, 3498. ISSN 1613-0073.
[ITAT 2023: Conference Information Technologies – Applications and Theory /23./. Tatranské Matliare (SK), 22.09.2023-26.09.2023]
Grant - others:Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
Institutional support: RVO:67985807
Keywords : black-box optimization * covariance matrix adaptation evolution strategy * surrogate modelling * Gaussian processes
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ceur-ws.org/Vol-3498/paper10.pdf
Permanent Link: https://hdl.handle.net/11104/0348533 - 2.0557942 - ÚI 2023 RIV US eng C - Conference Paper (international conference)
Pitra, Z. - Hanuš, M. - Koza, J. - Tumpach, Jiří - Holeňa, Martin
Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy.
Proceedings Of The 2021 Genetic And Evolutionary Computation Conference (Gecco'21). New York: Association for Computing Machinery, 2021 - (Chicano, F.), s. 528-536. ISBN 978-1-4503-8350-9.
[Gecco 2021: Genetic and Evolutionary Computation Conference. Lille / Online (FR), 10.07.2021-14.07.2021]
R&D Projects: GA ČR(CZ) GA18-18080S
Grant - others:Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * evolution control * CMA-ES
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://dx.doi.org/10.1145/3449639.3459358
Permanent Link: http://hdl.handle.net/11104/0331826 - 3.0533901 - ÚI 2021 RIV DE eng C - Conference Paper (international conference)
Dvořák, M. - Pitra, Zbyněk - Holeňa, Martin
Assessment of Surrogate Model Settings Using Landscape Analysis.
Proceedings of the 20th Conference Information Technologies - Applications and Theory. Aachen: Technical University & CreateSpace Independent Publishing, 2020 - (Holeňa, M.; Horváth, T.; Kelemenová, A.; Mráz, F.; Pardubská, D.; Plátek, M.; Sosík, P.), s. 81-89. CEUR Workshop Proceedings, 2718. ISSN 1613-0073.
[ITAT 2020: Information Technologies - Applications and Theory /20./. Oravská Lesná (SK), 18.09.2020-22.09.2020]
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : Black-box optimization * CMA-ES * Surrogate modelling * Gaussian process * Landscape analysis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2718/paper20.pdf
Permanent Link: http://hdl.handle.net/11104/0312131File Download Size Commentary Version Access 0533901-aw.pdf 4 740.4 KB CC BY 4.0 Publisher’s postprint open-access - 4.0508171 - ÚI 2020 RIV US eng C - Conference Paper (international conference)
Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy.
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. New York: ACM, 2019 - (López-Ibáñez, M.), s. 691-699. ISBN 978-1-4503-6111-8.
[GECCO 2019: The Genetic and Evolutionary Computation Conference. Prague (CZ), 13.07.2019-17.07.2019]
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Grant - others:GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process * landscape analysis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent Link: http://hdl.handle.net/11104/0299146 - 5.0506867 - ÚI 2020 RIV US eng A - Abstract
Bajer, Lukáš - Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Gaussian Process Surrogate Models for the CMA-ES.
GECCO '19. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2019. s. 17-18. ISBN 978-1-4503-6748-6.
[GECCO 2019: The Genetic and Evolutionary Computation Conference. 13.07.2019-17.07.2019, Prague]
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent Link: http://hdl.handle.net/11104/0298001 - 6.0478631 - ÚI 2018 RIV DE eng C - Conference Paper (international conference)
Repický, Jakub - Bajer, Lukáš - Pitra, Zbyněk - Holeňa, Martin
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models.
Proceedings ITAT 2017: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2017 - (Hlaváčová, J.), s. 136-143. CEUR Workshop Proceedings, V-1885. ISBN 978-1974274741. ISSN 1613-0073.
[ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./. Martinské hole (SK), 22.09.2017-26.09.2017]
R&D Projects: GA ČR GA17-01251S
Grant - others:GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process * CMA-ES
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-1885/136.pdf
Permanent Link: http://hdl.handle.net/11104/0274761File Download Size Commentary Version Access a0478631.pdf 3 761 KB Publisher’s postprint require - 7.0478629 - ÚI 2018 RIV DE eng C - Conference Paper (international conference)
Pitra, Zbyněk - Bajer, Lukáš - Repický, Jakub - Holeňa, Martin
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy.
Proceedings ITAT 2017: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2017 - (Hlaváčová, J.), s. 120-128. CEUR Workshop Proceedings, V-1885. ISBN 978-1974274741. ISSN 1613-0073.
[ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./. Martinské hole (SK), 22.09.2017-26.09.2017]
R&D Projects: GA ČR GA17-01251S
Grant - others:ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LO1611; GA MŠk(CZ) LM2010005
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process * CMA-ES
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-1885/120.pdf
Permanent Link: http://hdl.handle.net/11104/0274762File Download Size Commentary Version Access a0478629.pdf 5 1.2 MB Publisher’s postprint require - 8.0477789 - ÚI 2018 RIV US eng C - Conference Paper (international conference)
Pitra, Z. - Bajer, L. - Repický, J. - Holeňa, Martin
Comparison of Ordinal and Metric Gaussian Process Regression as Surrogate Models for CMA Evolution Strategy.
GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2017, s. 1764-1771. ISBN 978-1-4503-4939-0.
[GECCO 2017. Genetic and Evolutionary Computation Conference. Berlin (DE), 15.07.2017-19.07.2017]
R&D Projects: GA ČR GA17-01251S
Grant - others:GA MŠk(CZ) LO1611; ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2010005
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian-process regression
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent Link: http://hdl.handle.net/11104/0274013File Download Size Commentary Version Access a0477789.pdf 1 1.2 MB Publisher’s postprint require - 9.0477787 - ÚI 2018 US eng A - Abstract
Pitra, Z. - Bajer, L. - Repický, J. - Holeňa, Martin
Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy.
GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2017. s. 177-178. ISBN 978-1-4503-4939-0.
[GECCO 2017. Genetic and Evolutionary Computation Conference. 15.07.2017-19.07.2017, Berlin]
R&D Projects: GA ČR GA17-01251S
Grant - others:GA MŠk(CZ) LO1611; ČVUT(CZ) SGS17/193/OHK4/3T/14
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian-process regression
Subject RIV: IN - Informatics, Computer Science
Permanent Link: http://hdl.handle.net/11104/0274011File Download Size Commentary Version Access a0477787.pdf 2 668.9 KB Publisher’s postprint require - 10.0477762 - ÚI 2018 RIV US eng C - Conference Paper (international conference)
Pitra, Z. - Bajer, L. - Repický, J. - Holeňa, Martin
Overview of Surrogate-model Versions of Covariance Matrix Adaptation Evolution Strategy.
GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2017, s. 1622-1629. ISBN 978-1-4503-4939-0.
[GECCO 2017. Genetic and Evolutionary Computation Conference. Berlin (DE), 15.07.2017-19.07.2017]
R&D Projects: GA ČR GA17-01251S
Grant - others:GA MŠk(CZ) LO1611; ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2010005
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent Link: http://hdl.handle.net/11104/0274009File Download Size Commentary Version Access a0477762.pdf 2 957 KB Publisher’s postprint require