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Titlebook: Genetic Programming Theory and Practice V; Rick Riolo,Terence Soule,Bill Worzel Book 2008 Springer-Verlag US 2008 Algorithms.Multi-agent s

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书目名称Genetic Programming Theory and Practice V
编辑Rick Riolo,Terence Soule,Bill Worzel
视频video
概述Discusses hurdles in solving large-scale applications.Describes techniques including fitness and age layered populations, code reuse through caching, archives and libraries, Pareto optimization, pre-
丛书名称Genetic and Evolutionary Computation
图书封面Titlebook: Genetic Programming Theory and Practice V;  Rick Riolo,Terence Soule,Bill Worzel Book 2008 Springer-Verlag US 2008 Algorithms.Multi-agent s
描述.Genetic Programming Theory and Practice V was developed from the fifth 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...The work covers applications of GP to a wide variety of domains, including bioinformatics, symbolic regression for system modeling, financial modeling, circuit design and robot controllers. This volume 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 2008
关键词Algorithms; Multi-agent system; artificial intelligence; bioinformatics; circuit design; classification; e
版次1
doihttps://doi.org/10.1007/978-0-387-76308-8
isbn_softcover978-1-4419-4547-1
isbn_ebook978-0-387-76308-8Series ISSN 1932-0167 Series E-ISSN 1932-0175
issn_series 1932-0167
copyrightSpringer-Verlag US 2008
The information of publication is updating

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Data Acquisition and Preprocessing,a rigorous, detailed theory that is mature enough to guide the development of GP systems to solve specific problems contributes to this weakness. Thus, there remains an on-going need to both strengthen theory and to keep it closely tied to the practice of GP. The Genetic Programming Theory and Pract
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Luise Jacobs,Susanne Hensel-Börner we observed that those evolutions that started from the smallest subset sizes (10%) consistently led to results that are superior in terms of the goodness of fit, consistency between independent runs, and computational effort. Our experience indicates that solutions obtained using this approach are
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Database Benchmarking and Stress Testinguld be stronger than that of error, improving evolvability. We support these arguments with simulations using a logic function benchmark and a time series application. For a chaotic time series prediction problem, for instance, the proposed approach avoids familiar difficulties (premature convergenc
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