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Titlebook: Genetic Programming Theory and Practice XVII; Wolfgang Banzhaf,Erik Goodman,Bill Worzel Book 2020 Springer Nature Switzerland AG 2020 Gene

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书目名称Genetic Programming Theory and Practice XVII
编辑Wolfgang Banzhaf,Erik Goodman,Bill Worzel
视频video
概述Provides contributions describing cutting-edge work on the theory and applications of genetic programming (GP).Offers large-scale, real-world applications (big data) of GP to a variety of problem doma
丛书名称Genetic and Evolutionary Computation
图书封面Titlebook: Genetic Programming Theory and Practice XVII;  Wolfgang Banzhaf,Erik Goodman,Bill Worzel Book 2020 Springer Nature Switzerland AG 2020 Gene
描述.These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP.  In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results..
出版日期Book 2020
关键词Genetic Programming; Genetic Programming Theory; Genetic Programming Applications; Symbolic Regression;
版次1
doihttps://doi.org/10.1007/978-3-030-39958-0
isbn_softcover978-3-030-39960-3
isbn_ebook978-3-030-39958-0Series ISSN 1932-0167 Series E-ISSN 1932-0175
issn_series 1932-0167
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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https://doi.org/10.1007/978-3-642-13148-6on items to increase competitive presence of GP in Data Science business applications include: develop a successful marketing strategy toward statistical, machine/deep learning, and business communities; broaden application areas; improve professional development tools; and increase GP visibility and teaching in Data Science classes.
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https://doi.org/10.1007/978-3-662-26471-3rovide empirical evidence to support our model. The chapter closes with an outline of a modification of the standard GP algorithm that reinforces this bias by converging populations to fit schemata in an accelerated way.
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How Competitive Is Genetic Programming in Business Data Science Applications?,on items to increase competitive presence of GP in Data Science business applications include: develop a successful marketing strategy toward statistical, machine/deep learning, and business communities; broaden application areas; improve professional development tools; and increase GP visibility and teaching in Data Science classes.
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