Zobrazeno 1 - 10
of 582
pro vyhledávání: '"Kevin Sim"'
Autor:
Tilford,, Earl H.
Publikováno v:
The Journal of Military History, 1993 Jan 01. 57(1), 181-182.
Externí odkaz:
https://www.jstor.org/stable/2944262
Autor:
Bilton, Michael, Kevin Sim
Publikováno v:
The SHAFR Guide Online
Externí odkaz:
https://doi.org/10.1163/2468-1733_shafr_SIM170170062
Publikováno v:
Applications of Evolutionary Computation ISBN: 9783031302282
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b22bc50c31a0e5925dfb3582617acc61
https://doi.org/10.1007/978-3-031-30229-9_22
https://doi.org/10.1007/978-3-031-30229-9_22
Linear diagrams have been shown to be an effective method of representing set-based data. Moreover, a number of guidelines have been proven to improve the efficacy of linear diagrams. One of these guidelines is to minimise the number of line segments
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04d9fc55438e25de936cd9df960db926
We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3970eb6c91ff736862c8258c2595aee8
http://arxiv.org/abs/2203.13392
http://arxiv.org/abs/2203.13392
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031147135
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56b92e72b56868262bb1b0277949901d
https://doi.org/10.1007/978-3-031-14714-2_15
https://doi.org/10.1007/978-3-031-14714-2_15
Publikováno v:
CEC
Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ebeda91020e0ac0ab8765e0487b3c63f
https://doi.org/10.1109/cec45853.2021.9504989
https://doi.org/10.1109/cec45853.2021.9504989
Publikováno v:
GECCO
In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbf995df454a28f339061c5f0be993df
https://doi.org/10.1145/3377930.3390224
https://doi.org/10.1145/3377930.3390224
Publikováno v:
GECCO
We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In contrast to the majority of work in algorithm-selection, the approach does
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c52eda41eaf3091c02872b65d47f63cd
https://doi.org/10.1145/3321707.3321845
https://doi.org/10.1145/3321707.3321845
Publikováno v:
Agricultural and Forest Meteorology
Agricultural and Forest Meteorology, Elsevier Masson, 2019, 265, pp.16-29. ⟨10.1016/j.agrformet.2018.10.022⟩
Agricultural and Forest Meteorology, Elsevier Masson, 2019, 265, pp.16-29. ⟨10.1016/j.agrformet.2018.10.022⟩
This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed indi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ee72e202aee64c28134e07f8ba474c