Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Anna Ouskova Leonteva"'
Autor:
Anna Ouskova Leonteva, Michel Risser, Radia Hamane, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet
Publikováno v:
GECCO '22: Genetic and Evolutionary Computation Conference
GECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston Massachusetts, United States. pp.739-742, ⟨10.1145/3520304.3529055⟩
GECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston Massachusetts, United States. pp.739-742, ⟨10.1145/3520304.3529055⟩
International audience; Active Magnetic Regenerator (AMR) refrigeration is an innovate technology, which can reduce energy consumption and the depletion of the ozone layer. However, to develop a commercially applicable design of the AMR model is stil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0ec9dca54d80e4a09f03da16f691b98
https://hal.science/hal-03902551/document
https://hal.science/hal-03902551/document
Autor:
Industry, Baku, Azerbaijan, Anna Ouskova Leonteva, Ulviya Abdulkarimova, Anne Jeannin-Girardon, Pierre Collet
Publikováno v:
Azerbaijan Journal of High Performance Computing. 2:122-140
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b30f5d6d782d9b263249157426ba862
https://doi.org/10.32010/26166127.2019.2.2.122.140
https://doi.org/10.32010/26166127.2019.2.2.122.140
Autor:
Radia Hamane, Pierre Parrend, Pierre Collet, Michel Risser, Anne Jeannin-Girardon, Anna Ouskova Leonteva
Publikováno v:
CEC
International Conference on the IEEE Congress on Evolutionary
International Conference on the IEEE Congress on Evolutionary, Jun 2021, En ligne, France
International Conference on the IEEE Congress on Evolutionary
International Conference on the IEEE Congress on Evolutionary, Jun 2021, En ligne, France
Magnetic refrigeration (MR) is an alternative technology to conventional vapour compression with a high potential to reduce energy consumption and greenhouse gases. The working principle of MR is based on the property of magneto caloric materials (MC
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5be279bbdd31502a99c8ab01247e1ff9
https://hal.science/hal-03321673
https://hal.science/hal-03321673
Autor:
Christian Rolando, Rabih Amhaz, Marc Haegelin, Younes Monjid, Anna Ouskova Leonteva, Pierre Collet, Igor Santos Peretta, Ulviya Abdulkarimova
Publikováno v:
GECCO Companion
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
In this paper, we use a real valued genetic algorithm (GA) to model a large noisy periodic signal. The information that must be extracted are the amplitude, angular velocity and phase of the sines composing the signal. The algorithm outperforms the F
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef78d474230ce2e6f78738b20140efa1
https://doi.org/10.1145/3377929.3390008
https://doi.org/10.1145/3377929.3390008
Autor:
Anne Jeannin-Girardon, Ulviya Abdulkarimova, Pierre Collet, Anna Ouskova Leonteva, Tobias M. Wintermantel, Pierre Parrend
Publikováno v:
GECCO Companion
This paper presents a novel multi-threaded quantum inspired optimization algorithm targeted at global search in continuous domains. The proposed approach is based on a Diffusion Monte Carlo (DMC) physical model and is characterized by a set of parall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee546c0660bbbb23d5f70a2be75c2554
https://doi.org/10.1145/3377929.3389990
https://doi.org/10.1145/3377929.3389990
Autor:
Michel Risser, Anne Jeannin-Girardon, Anna Ouskova Leonteva, Pierre Collet, Ulviya Abdulkarimova, Pierre Parrend
Publikováno v:
Theory and Practice of Natural Computing ISBN: 9783030629991
TPNC
TPNC
Quantum-inspired algorithms are efficient for solving global search optimization problems. Nevertheless, their application is limited by two main requirements: a knowledge of a cost function and a big computational effort. To address both limitations
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1dee506b0f44969f395ff2c6ef10e072
https://doi.org/10.1007/978-3-030-63000-3_8
https://doi.org/10.1007/978-3-030-63000-3_8
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030457143
This paper proposes a fast evolutionary algorithm for large-scale multi-objective optimization problems (MOPs), which widely exist in real-world applications [3, 6]. Many well-established multi-objective evolutionary algorithms (MOEAs) can not ensure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e3f3825bc5a914b9dbdb6740dc683ac
https://doi.org/10.1007/978-3-030-45715-0_7
https://doi.org/10.1007/978-3-030-45715-0_7