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Titlebook: Genetic Programming Theory and Practice II; Una-May O’Reilly,Tina Yu,Bill Worzel Book 2005 Springer-Verlag US 2005 Algorithms.Automat.algo

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书目名称Genetic Programming Theory and Practice II
编辑Una-May O’Reilly,Tina Yu,Bill Worzel
视频video
概述Prepared by the Center for the Study of Complex Systems at the University of Michigan.Combines the theory and current industry practice of Genetic Programming.Includes supplementary material:
丛书名称Genetic Programming
图书封面Titlebook: Genetic Programming Theory and Practice II;  Una-May O’Reilly,Tina Yu,Bill Worzel Book 2005 Springer-Verlag US 2005 Algorithms.Automat.algo
描述The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other‘s chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the b
出版日期Book 2005
关键词Algorithms; Automat; algorithm; computer; genetic programming; learning; machine learning; programming; algo
版次1
doihttps://doi.org/10.1007/b101112
isbn_softcover978-1-4419-3589-2
isbn_ebook978-0-387-23254-6Series ISSN 1566-7863
issn_series 1566-7863
copyrightSpringer-Verlag US 2005
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Using Genetic Programming in Industrial Statistical Model Building,odology for using Genetic Programming in statistical modeling of designed and undesigned data is described and illustrated with successful industrial applications. As a result of the synergistic efforts, the building technique has been improved and the model development cost and time can be signific
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Considering the Roles of Structure in Problem Solving by Computer,ore difficult than others for genetic programming to solve. This chapter subsequently summarizes my group’s current theoretical work at the University of Michigan and extends the implications of that work to real-world problem solving.
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Favourable Biasing of Function Sets Using Run Transferable Libraries,ith standard programming libraries as they provide a suite of functions that can not only be used across several runs on a particular problem, but also to aid in the scaling of a system to more difficult instances of a problem. This is achieved by . a library on a relatively simple instance of a pro
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ACGP: Adaptable Constrained Genetic Programming,the user is often forced to provide a large set, thus enlarging the search space — often resulting in reducing the search efficiency. Moreover, based on heuristics, syntactic constraints, or data typing, a given subtree may be undesired or invalid in a given context. Typed Genetic Programming method
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