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Titlebook: Genetic Programming Theory and Practice IV; Rick Riolo,Terence Soule,Bill Worzel Book 2007 Springer-Verlag US 2007 Automat.Boosting.algori

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發(fā)表于 2025-3-21 16:21:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Genetic Programming Theory and Practice IV
編輯Rick Riolo,Terence Soule,Bill Worzel
視頻videohttp://file.papertrans.cn/383/382599/382599.mp4
概述Discusses the hurdles faced in solving large-scale, cutting edge applications.Describes promising techniques, including fitness approximation, Pareto optimization, cooperative teams, solution caching,
叢書名稱Genetic and Evolutionary Computation
圖書封面Titlebook: Genetic Programming Theory and Practice IV; Rick Riolo,Terence Soule,Bill Worzel Book 2007 Springer-Verlag US 2007 Automat.Boosting.algori
描述.Genetic Programming Theory and Practice IV. was developed from the fourth workshop at the University of Michigan’s Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application...This volume represents a watershed moment in the GP field in that GP has begun to move from hand-crafted software used primarily in academic research, to an engineering methodology applied to commercial applications. It is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence..
出版日期Book 2007
關(guān)鍵詞Automat; Boosting; algorithm; algorithms; artificial intelligence; classification; complex system; genetic
版次1
doihttps://doi.org/10.1007/978-0-387-49650-4
isbn_softcover978-1-4419-4123-7
isbn_ebook978-0-387-49650-4Series ISSN 1932-0167 Series E-ISSN 1932-0175
issn_series 1932-0167
copyrightSpringer-Verlag US 2007
The information of publication is updating

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發(fā)表于 2025-3-22 00:04:03 | 只看該作者
Data Mining: Statistical Analysisn to interactive evolution of pen stroke drawings where no true fitness function is known. These results demonstrate coevolution’s ability to infer a fitness landscape of a user’s preference while minimizing user interaction.
板凳
發(fā)表于 2025-3-22 02:50:44 | 只看該作者
https://doi.org/10.1007/978-3-642-57681-2on of genetic programming for the automatic synthesis of designs. Third, the paper discusses certain domain-specific adjustments in technique that may increase the efficiency of the automated process in a particular domain. Fourth, the paper discusses several technique issues that have arisen in more than one domain.
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Coevolving Fitness Models for Accelerating Evolution and Reducing Evaluations,n to interactive evolution of pen stroke drawings where no true fitness function is known. These results demonstrate coevolution’s ability to infer a fitness landscape of a user’s preference while minimizing user interaction.
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發(fā)表于 2025-3-22 13:05:35 | 只看該作者
Multi-Domain Observations Concerning the Use of Genetic Programming to Automatically Synthesize Humon of genetic programming for the automatic synthesis of designs. Third, the paper discusses certain domain-specific adjustments in technique that may increase the efficiency of the automated process in a particular domain. Fourth, the paper discusses several technique issues that have arisen in more than one domain.
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Grundlagen, Einsatzbereiche, Modelleassifiers to bagging and boosting with crossvalidation and parameter optimization requires more computing power. A special-purpose search processor for fitness evaluation renders boosted genetic programming practical for our purposes.
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