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Titlebook: Genetic Programming Theory and Practice XVI; Wolfgang Banzhaf,Lee Spector,Leigh Sheneman Book 2019 Springer Nature Switzerland AG 2019 Gen

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書目名稱Genetic Programming Theory and Practice XVI
編輯Wolfgang Banzhaf,Lee Spector,Leigh Sheneman
視頻videohttp://file.papertrans.cn/383/382612/382612.mp4
概述Provides papers 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 domains, in
叢書名稱Genetic and Evolutionary Computation
圖書封面Titlebook: Genetic Programming Theory and Practice XVI; Wolfgang Banzhaf,Lee Spector,Leigh Sheneman Book 2019 Springer Nature Switzerland AG 2019 Gen
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. Topics in this volume include: evolving developmental programs for neural networks solving multiple problems, tangled program, transfer learning and outlier detection using GP, program search for machine learning pipelines in reinforcement learning, automatic programming with GP, new variants of GP, like SignalGP, variants of lexicase selection, and symbolic regression and classification techniques. 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 2019
關鍵詞Genetic Programming; Genetic Programming Theory; Genetic Programming Applications; Symbolic Regression;
版次1
doihttps://doi.org/10.1007/978-3-030-04735-1
isbn_ebook978-3-030-04735-1Series ISSN 1932-0167 Series E-ISSN 1932-0175
issn_series 1932-0167
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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https://doi.org/10.1007/978-3-322-89690-2ore fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical pro
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Exploring Genetic Programming Systems with MAP-Elites,rks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that
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What Else Is in an Evolved Name? Exploring Evolvable Specificity with SignalGP,ore fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical pro
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Deutsches Krankenhausinstitut Düsseldorfional problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a b
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