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Titlebook: Bio-Inspired Computing: Theories and Applications; 16th International C Linqiang Pan,Zhihua Cui,Lianghao Li Conference proceedings 2022 Spr

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發(fā)表于 2025-3-21 19:44:50 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bio-Inspired Computing: Theories and Applications
期刊簡(jiǎn)稱16th International C
影響因子2023Linqiang Pan,Zhihua Cui,Lianghao Li
視頻videohttp://file.papertrans.cn/187/186341/186341.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Bio-Inspired Computing: Theories and Applications; 16th International C Linqiang Pan,Zhihua Cui,Lianghao Li Conference proceedings 2022 Spr
影響因子This two-volume set (CCIS 1565 and CCIS 1566) constitutes selected and revised papers from the?16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021, held in Taiyuan, China, in December 2021.?.The 67 papers presented were thoroughly reviewed and selected from 211 submissions. The papers are organized in the following topical sections: ?evolutionary computation and swarm intelligence; DNA and molecular computing; ?machine learning and computer vision..
Pindex Conference proceedings 2022
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Black Widow Spider Algorithm Based on Differential Evolution and Random Disturbancential evolution strategy is introduced to avoid unnecessary exploration. At the same time, the random disturbance factor is used to improve the local exploitation performance of the black widow algorithm, and the memory function is added to the individual to improve the population‘s reproductive str
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A Cuckoo Quantum Evolutionary Algorithm for the Graph Coloring Problem A cuckoo quantum evolutionary algorithm (CQEA) is proposed for the GCP, which is based on the framework of quantum-inspired evolutionary algorithm. To reduce iterations for the search of the chromatic number, the initial quantum population is generated with random initialization assisted by inherit
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Feature Selection Algorithm Based on Discernibility Matrix and Fruit Fly Optimization can quickly search for feature subsets, effectively reducing the computational workload; However, in some decision tables of this type of method, there were also problems such as the searched feature subset containing redundant features, which led to a decrease in accuracy. Aiming at such problems
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Implementation and Application of NSGA-III Improved Algorithm in Multi-objective Environmentr more objectives) has gradually become a hot spot. NSGA-III algorithm is effective in dealing with evolutionary multi-objective optimization problems. In this paper, we recognize some advantages of the existing NSGA-III algorithm and make some improvements. The improved NSGA-III algorithm has highe
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