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Titlebook: Bioinspired Optimization Methods and Their Applications; 10th International C Marjan Mernik,Tome Eftimov,Matej ?repin?ek Conference proceed

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發(fā)表于 2025-3-21 16:11:31 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bioinspired Optimization Methods and Their Applications
期刊簡(jiǎn)稱10th International C
影響因子2023Marjan Mernik,Tome Eftimov,Matej ?repin?ek
視頻videohttp://file.papertrans.cn/188/187257/187257.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Bioinspired Optimization Methods and Their Applications; 10th International C Marjan Mernik,Tome Eftimov,Matej ?repin?ek Conference proceed
影響因子This book constitutes the refereed proceedings of the 10th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2022, held in Maribor, Slovenia, in November 2022..The 19 full papers presented in this book were carefully reviewed and selected from 23 submissions..The papers in this BIOMA proceedings specialized in bioinspired algorithms as a means for solving the optimization problems and came in two categories: theoretical studies and methodology advancements on the one hand, and algorithm adjustments and their applications on the other..
Pindex Conference proceedings 2022
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Kidney and Kidney Tumor Segmentationree with low computational complexity, resulting in an accelerated surrogate process. Moreover, a new EMOEA is proposed by integrating spherical search as the core optimizer with the proposed selection scheme. Over a wide variety of benchmark problems, we show that the proposed method outperforms se
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Steven J. Kraus MD,Sara M. O’Hara MDure portfolios as important for the performance prediction task. We provided explanations for the behaviour of the algorithms in this scenario, by identifying the set of most important features and further using this information to compare the different algorithms and find algorithms with similar ex
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Transplant Considerations in the Newborn, comparable to XCSF’s while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits. This increased control can aid in subsequently providing explanations for both training and final structure of the model.
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Studies in Computational Intelligencemethods such as the Bayesian comparison of classifiers and the Mann-Whitney-U-Test. However, these improvements come with the cost of more mutation steps needed which in turn lengthens the training time. The third variant, in which . is decreased, does not differ from the second mutation strategy li
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https://doi.org/10.1007/978-3-031-36981-0iants, although still undoubtedly bringing value on these problems, does hint at complex interactions with already integrated enhancements, suggesting that extending already enhanced algorithm variants is not simple, to say the least.
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https://doi.org/10.1007/978-3-031-36981-0 use it to evolve highly nonlinear balanced Boolean functions. We organize our experiments around two research questions, namely if local search (1) improves the convergence speed of GA, and (2) decreases the population diversity. Surprisingly, while our results answer affirmatively the first questi
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