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Titlebook: Computational Intelligence in Optimization; Applications and Imp Yoel Tenne,Chi-Keong Goh Book 2010 Springer-Verlag Berlin Heidelberg 2010

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樓主: 孵化
21#
發(fā)表于 2025-3-25 06:51:42 | 只看該作者
https://doi.org/10.1007/978-3-030-74458-8implicity of their processing elements (PEs), modularity of design, regular and nearest neighbor interconnections between the PEs, high-level of pipelinability, small chip-area and low-power consumption. In systolic arrays, the desired data is pumped rhythmically in a regular interval across the PEs
22#
發(fā)表于 2025-3-25 07:34:06 | 只看該作者
23#
發(fā)表于 2025-3-25 12:36:08 | 只看該作者
Book 2010on to challenging real-world problems. Topics covered include: Intelligent agent-based algorithms, Hybrid intelligent systems, Cognitive and evolutionary robotics, Knowledge-Based Engineering, fuzzy sets and systems, Bioinformatics and Bioengineering, Computational finance and Computational economic
24#
發(fā)表于 2025-3-25 19:12:51 | 只看該作者
25#
發(fā)表于 2025-3-25 23:57:15 | 只看該作者
26#
發(fā)表于 2025-3-26 00:18:34 | 只看該作者
Refugees in Neoliberal Universitiestimization problems of practical interest that arise in the fields of machine learning (pruning of ensembles of classifiers), quantitative finance (portfolio selection), time-series modeling (index tracking) and statistical data analysis (sparse principal component analysis).
27#
發(fā)表于 2025-3-26 05:22:50 | 只看該作者
Antoinette Geagea,Judith MacCallumace methods on a pre-selected set of test functions. We aim to show that there are number of techniques which can be used to tackle difficult problems and we also demonstrate that a careful choice of response surface methods is important when carrying out surrogate assisted multiobjective search.
28#
發(fā)表于 2025-3-26 09:37:01 | 只看該作者
Peter A. Wilderer,Michael von Hauffzation, 2) SVM Parallelization, 3) Neighbor Search and 4) Evaluation Caching. All the four strategies improve the respective aspects of the feature selection algorithm and contribute collectively towards higher computational throughput.
29#
發(fā)表于 2025-3-26 16:39:31 | 只看該作者
30#
發(fā)表于 2025-3-26 19:59:11 | 只看該作者
Multi-Objective Optimization Using Surrogates,ace methods on a pre-selected set of test functions. We aim to show that there are number of techniques which can be used to tackle difficult problems and we also demonstrate that a careful choice of response surface methods is important when carrying out surrogate assisted multiobjective search.
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