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Titlebook: Learning and Intelligent Optimization; 11th International C Roberto Battiti,Dmitri E. Kvasov,Yaroslav D. Serge Conference proceedings 2017

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樓主: Inveigle
11#
發(fā)表于 2025-3-23 13:30:16 | 只看該作者
12#
發(fā)表于 2025-3-23 14:48:55 | 只看該作者
Interior Point and Newton Methods in Solving High Dimensional Flow Distribution Problems for Pipe Neethod works much slower then Newton Method on the matrices of big size, such approach allows us to obtain good starting point for this method by using comparatively fast Newton Method. Results of numerical experiments are presented.
13#
發(fā)表于 2025-3-23 18:24:09 | 只看該作者
14#
發(fā)表于 2025-3-23 23:38:53 | 只看該作者
A New Constructive Heuristic for the No-Wait Flowshop Scheduling Problemheduling Problem. This proposed heuristic is based on observations on the structure of best solutions of small instances as well as on analyzes of efficient constructive heuristics principles of the literature. Experiments have been conducted and results show the efficiency of the proposed heuristic compared to ones from the literature.
15#
發(fā)表于 2025-3-24 04:14:49 | 只看該作者
16#
發(fā)表于 2025-3-24 10:35:20 | 只看該作者
Conference proceedings 2017tion, LION 11, held in Nizhny,Novgorod, Russia, in June 2017.. .The 20 full papers (among these one GENOPT paper) and 15 short papers presented have been carefully reviewed and selected from 73 submissions. The papers explore the advanced research developments in such interconnected fields as mathem
17#
發(fā)表于 2025-3-24 10:48:18 | 只看該作者
18#
發(fā)表于 2025-3-24 17:46:37 | 只看該作者
0302-9743 nts in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning..978-3-319-69403-0978-3-319-69404-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
19#
發(fā)表于 2025-3-24 20:02:49 | 只看該作者
20#
發(fā)表于 2025-3-24 23:50:32 | 只看該作者
Automatic Configuration of Kernel-Based Clustering: An Optimization Approachalgorithm. Both Gaussian Processes and Random Forests have been considered to fit the surrogate model of the objective function, while results from a simple grid search have been considered as baseline.
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