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遗传编程理论与实践II:自动编程方法探索

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"Genetic Programming Theory and Practice II - John Koza.pdf" 本书是"遗传程序设计系列"的一部分,由John Koza编辑,专注于遗传程序设计的理论与实践。书中主要探讨了遗传程序设计(Genetic Programming, GP)的概念及其在各种领域中的应用。 在介绍遗传程序设计之前,第一章阐述了两个主要观点。第二章展示了如何将多个不同领域的看似不同的问题视为程序归纳问题。第三章介绍了传统的遗传算法,并引入了遗传算法和遗传程序设计共有的基本术语,对遗传算法不熟悉的读者可以从这一章开始阅读。第四章讨论了固定长度字符串的常规遗传算法所面临的表示问题,以及处理更复杂和灵活结构的变体。此外,书中还介绍了LISP编程语言,因为该书假设读者没有LISP的基础知识。 第五章提供了遗传程序设计范式的非正式概述,第六章详细介绍了遗传程序设计的技术。部分读者可能更愿意依赖第五章,将第六章的详细讨论留到阅读第七章及后续包含实例的章节之后。第七章详述了如何将遗传程序设计应用于四个入门示例,为后续章节中描述的问题奠定了基础。 第八章探讨了解决特定问题时遗传程序设计所需的计算处理量。第九章证明了遗传程序设计的结果并非随机搜索的结果。从第十章到第二十一章,书中详细展示了如何使用遗传程序设计解决来自各个领域的各种问题,包括符号回归、控制和最优控制、自组织行为进化、子概括演化、熵驱动进化、策略演化、协同演化、分类演化、迭代和递归演化、具有句法结构的程序演化、通过自动函数定义演化构建模块以及通过分层自动函数定义演化层次结构构建模块。 第二十二章讨论了在并行计算机架构上实现遗传程序设计。第二十三章讨论了遗传程序设计在噪声、采样、变化和损坏方面的鲁棒性。第二十四章涉及额外变量和函数的作用。第二十五章呈现了一些关于遗传程序设计操作问题的实验结果。第二十六章总结了使用遗传程序设计前的五个主要步骤。第二十七章将遗传程序设计与其他机器学习范式进行了比较。第二十八章讨论了自我复制、性繁殖和自我改进计算机程序的自发涌现。第二十九章为结论。 十篇附录涵盖了遗传程序设计范式的计算机实现以及与操作问题相关的各种实验结果。附录A讨论了我们遗传程序设计计算机实现的交互式用户界面。附录B提供了实现遗传程序设计所需的具体问题部分的简单LISP代码,为三个不同问题提供了三个不同的技术示例。附录C提供了代码核心(即问题独立部分)的简单LISP代码。 这本书深入浅出地介绍了遗传程序设计的理论、实践方法以及其在复杂适应系统中的应用,对于希望理解和应用遗传程序设计的读者来说是一本宝贵的资源。

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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. Specific topics addressed in the book include: the hurdles faced in solving large-scale, cutting edge applications promising techniques, including fitness and age layered populations, code reuse through caching, archives and run transferable libraries, Pareto optimization, and pre- and post-processing the use of information theoretic measures and ensemble techniques approaches to help GP create trustable solutions the use of expert knowledge to guide GP ways to make GP tools more accessible to the non-GP-expert practical methods for understanding and choosing between the recent proliferation of techniques for improving GP performance the potential for GP to undergo radical changes to accommodate the expanded understanding of biological genetics and evolution 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.
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Springer.Genetic.Programming.Theory.And.Practice.II (遗传算法理论与实践 II) 一本系统介绍遗传算法理论和实际应用的经典好书 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 realworld 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-2004/. 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 book would be possible. We also thank our keynote speakers Lawrence (“Dave”) Davis of NuTech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants. The workshop received support from these sources: The Center for the Study of Complex Systems (CSCS); Third Millennium Venture Capital Limited; State Street Global Advisors, Boston, MA; Biocomputing and Developmental Systems Group, Computer Science and Information Systems, University of Limerick; Christopher T. May, RedQueen Capital Management; and Dow Chemical, Core R&D/Physical Sciences. xiv GENETIC PROGRAMMING THEORY AND PRACTICE II We thank all of our sponsors for their kind and generous support for the workshop and GP research in general. A number of people made key contributions to running the workshop and assisting the attendees while they were in Ann Arbor. Foremost among them was Howard Oishi. Howard was assisted by Mike Charters. We also thank Bill Tozier for helping with reading and copy-editing chapters. Melissa Fearon’s editorial efforts were invaluable from the initial plans for the book through its final publication. Thanks also to Deborah Doherty of Kluwer for helping with various technical publishing issues. Finally, we thank Carl Simon, Director of CSCS, for his support for this endeavor from its very inception.
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AIAlchemist
2025.06.06
书中通过多个实例展示了遗传编程解决实际问题的强大能力,涵盖从符号回归到复杂行为的进化等多个领域。🍛
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chenbtravel
2025.04.29
附录部分为读者提供了遗传编程范式计算机实现的详细信息,以及各种实验结果,对实践者尤其有帮助。
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蓝洱
2025.04.10
对于希望探索遗传编程与机器学习其他范式对比的读者来说,本书提供了有价值的参考和深入的分析。🍜
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yxldr
2025.03.22
章节设置合理,由浅入深地介绍了遗传编程的各个方面,尤其是对LISP语言的介绍,对理解遗传编程至关重要。
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VashtaNerada
2025.03.16
这本关于遗传编程的书籍深入浅出,适合初学者和进阶读者,内容覆盖了遗传算法的基本原理及其应用。
drjiachen
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