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Titlebook: Artificial Evolution; Third European Confe Jin-Kao Hao,Evelyne Lutton,Dominique Snyers Conference proceedings 1998 Springer-Verlag Berlin H

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期刊全稱Artificial Evolution
期刊簡(jiǎn)稱Third European Confe
影響因子2023Jin-Kao Hao,Evelyne Lutton,Dominique Snyers
視頻videohttp://file.papertrans.cn/163/162021/162021.mp4
學(xué)科分類(lèi)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Artificial Evolution; Third European Confe Jin-Kao Hao,Evelyne Lutton,Dominique Snyers Conference proceedings 1998 Springer-Verlag Berlin H
影響因子This book constitutes the thoroughly refereed post-conference proceedings of the Third European Conference on Artificial Evolution, AE ‘97, held in Nimes, France, in October 1997. The volume presents 22 revised full papers carefully selected for inclusion together with an invited survey on the state-of-the-art in scatter search by Fred Glover. The papers are organized in topical sections on genetic-algorithm operators, applications in various areas, theoretical issues, methodologies, and evolvable hardware and robotics.
Pindex Conference proceedings 1998
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978-3-540-64169-8Springer-Verlag Berlin Heidelberg 1998
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Aaron Nicolson,Jason Dowling,Bevan Koopmanolving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, these methods use strategies for combining solution vectors that have proved effective for scheduling, routing, financial product des
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Single Crystal Fabrication/Orientationthe . sleep during which the system is widely disconnected from its environment. The dream phase allows the population to reorganize and maintain a needed diversity. Experiments show that dreamy genetic algorithms improve on standard genetic algorithm, for both stationary (deceptive) and non-station
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Experiment and the Molecularity of Meaning,ng his interactions with a digital assistant. This study is done in the framework of “broadcasting” on the Internet. In our experiment, a multi-agent system with a Genetic Algorithm is used to globally optimize a user‘s selection of “channels” among a very large number of choices. We show that this
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