Zobrazeno 1 - 10
of 847
pro vyhledávání: '"Chicano, F."'
Publikováno v:
In Expert Systems With Applications 15 December 2020 161
Publikováno v:
In Information Sciences November 2019 503:255-273
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
2021 Genetic and Evolutionary Computation Conference, GECCO 2021
GECCO Companion
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Gecco '21: proceedings of the genetic and evolutionary computation conference companion, 1344-1345. New York: ACM
STARTPAGE=1344;ENDPAGE=1345;TITLE=Gecco '21: proceedings of the genetic and evolutionary computation conference companion
GECCO Companion
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Gecco '21: proceedings of the genetic and evolutionary computation conference companion, 1344-1345. New York: ACM
STARTPAGE=1344;ENDPAGE=1345;TITLE=Gecco '21: proceedings of the genetic and evolutionary computation conference companion
A new acquisition function is proposed for solving robust optimization problems via Bayesian Optimization. The proposed acquisition function reflects the need for the robust instead of the nominal optimum, and is based on the intuition of utilizing t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09a6a3e603aa55ab2dca3e80d9063605
https://hdl.handle.net/1887/3248671
https://hdl.handle.net/1887/3248671
Publikováno v:
GECCO Companion
GECCO'21: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1596-1604. ACM
STARTPAGE=1596;ENDPAGE=1604;TITLE=GECCO'21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
GECCO'21: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1596-1604. ACM
STARTPAGE=1596;ENDPAGE=1604;TITLE=GECCO'21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
Optimal Lens Design constitutes a fundamental, long-standing real-world optimization challenge. Potentially large number of optima, rich variety of critical points, as well as solid understanding of certain optimal designs per simple problem instance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e38bd522adc9b3e51a58116ea6537815
http://arxiv.org/abs/2105.10541
http://arxiv.org/abs/2105.10541
Publikováno v:
GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference, 678-686. New York, U.S.A.: Association for Computing Machinery
STARTPAGE=678;ENDPAGE=686;TITLE=GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference
GECCO
STARTPAGE=678;ENDPAGE=686;TITLE=GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference
GECCO
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too complex to encode manually, generative mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05afcd13e1effaf7d9e89fa74fe34f42
Publikováno v:
GECCO '21: Proceedings of the genetic and evolutionary computation conference companion, 151-152. New York: ACM
STARTPAGE=151;ENDPAGE=152;TITLE=GECCO '21: Proceedings of the genetic and evolutionary computation conference companion
GECCO Companion
STARTPAGE=151;ENDPAGE=152;TITLE=GECCO '21: Proceedings of the genetic and evolutionary computation conference companion
GECCO Companion
Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem. In this work, we propose a zero-shot method to m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c09dc0bcde47ddf7f1a63249a4ab0cb
http://hdl.handle.net/1887/3277252
http://hdl.handle.net/1887/3277252
Publikováno v:
GECCO Companion
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1234-1242. ACM
STARTPAGE=1234;ENDPAGE=1242;TITLE=Proceedings of the Genetic and Evolutionary Computation Conference Companion
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1234-1242. ACM
STARTPAGE=1234;ENDPAGE=1242;TITLE=Proceedings of the Genetic and Evolutionary Computation Conference Companion
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::312179bca0a50c6ae494c4fad915c332
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.