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Titlebook: Applications of Evolutionary Computing; EvoWorkshop 2003: Ev Stefano Cagnoni,Colin G. Johnson,Emma Hart Conference proceedings 2003 Springe

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發(fā)表于 2025-3-21 19:19:11 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Applications of Evolutionary Computing
期刊簡稱EvoWorkshop 2003: Ev
影響因子2023Stefano Cagnoni,Colin G. Johnson,Emma Hart
視頻videohttp://file.papertrans.cn/160/159417/159417.mp4
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Applications of Evolutionary Computing; EvoWorkshop 2003: Ev Stefano Cagnoni,Colin G. Johnson,Emma Hart Conference proceedings 2003 Springe
影響因子.?..This book constitutes the joint refereed proceedings of six workshops, EvoWorkshops 2003, held together with EuroGP 2003 in Essex, UK in April 2003...The 63 revised full papers presented were carefully reviewed and selected from a total of 109 submissions. In accordance with the six workshops covered , the papers are organized in topical sections on bioinformatics, combinatorial optimization, image analysis and signal processing, evolutionary music and art, evolutionary robotics, and scheduling and timetabling..
Pindex Conference proceedings 2003
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AdoMet:Arsenic(III) Methyltransferaseindependent set problem. The penalty approach is used for both algorithms and tuning of the penalty function is considered in the first part of the paper. In the second part we give some reasons why one could expect the competitive performance of the (1+1)-EA. The results of computational experiment are presented.
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Encyclopedia of Metalloproteinse simplest model of this problem, which is already NP-complete, is dealt with to compare also different crossover operators. Experimental results support the hypothesis that the integer based representation with a specific crossover operator outperforms the more traditional binary one for a very specific set of instances.
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https://doi.org/10.1007/3-540-36605-9Augmented Reality; bioinformatics; combinatorial optimization; image analysis; optimization; robot; roboti
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Promoter Prediction with a GP-Automatonevolved in the transition; and it combines motif matches via logical functions that it also stores and evolves in each transition. This scheme learns to predict promoters in human genome. The experiments reported use 5-fold cross validation.
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/159417.jpg
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