群体智能是近年来发展迅速的人工智能学科领域.通过研究分散,自组织的动物群体和人类社会的智能行为, 学者们提出了许多迥异于传统思路的智能算法, 很好地解决了不少原来非常棘手的复杂工程问题.与蚁群算法齐名的粒子群优化(particle swarm optimization, 简称PSO)算法就是其中最受瞩目,应用最为广泛的成果之一. 本书由粒子群优化算法之父撰写,是该领域毋庸置疑的经典著作.作者提出,人类智能来源于社会环境中个体之间的交互,这种智能模型可以有效的应用到人工智能系统中去. 书中首先从社会心理学, 认知科学和演化计算等多个角度阐述了这种新方法的基础,然后详细说明了应用这些理论和模型所得出的新的计算智能方法--粒子群优化,进而深入探讨了将粒子群优化应用于广泛的工程问题.

Gerd Gigerenzer's influential work examines the rationality of individuals not from the perspective of logic or probability, but from the point of view of adaptation to the real world of human behavior and interaction with the environment. Seen from this perspective, human behavior is more rational than it might otherwise appear. This work is extremely influential and has spawned an entire research program. This volume (which follows on a previous collection, Adaptive Thinking, also published by OUP) collects his most recent articles, looking at how people use "fast and frugal heuristics" to calculate probability and risk and make decisions. It includes a newly writen, substantial introduction, and the articles have been revised and updated where appropriate. This volume should appeal, like the earlier volumes, to a broad mixture of cognitive psychologists, philosophers, economists, and others who study decision making.

Every day, we use our computers to perform remarkable feats. A simple web search picks out a handful of relevant needles from the world's biggest haystack: the billions of pages on the World Wide Web. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers; and, we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease? This is the first book to answer that question in language anyone can understand, revealing the extraordinary ideas that power our PCs, laptops, and smartphones. Using vivid examples, John MacCormick explains the fundamental "tricks" behind nine types of computer algorithms, including artificial intelligence (where we learn about the "nearest neighbor trick" and "twenty questions trick"), Google's famous PageRank algorithm (which uses the "random surfer trick"), data compression, error correction, and much more. These revolutionary algorithms have changed our world: this book unlocks their secrets, and lays bare the incredible ideas that our computers use every day.

搜索引擎作为互联网发展中至关重要的一种应用，已经成为互联网各个领域的制高点，其重要性不言而喻。搜索引擎领域也是互联网应用中不多见的以核心技术作为其命脉的领域，搜索引擎各个子系统是如何设计的？这成为广大技术人员和搜索引擎优化人员密切关注的内容。 本书的最大特点是内容新颖全面而又通俗易懂。对于实际搜索引擎所涉及的各种核心技术都有全面细致的介绍，除了作为搜索系统核心的网络爬虫、索引系统、排序系统、链接分析及用户分析外，还包括网页反作弊、缓存管理、网页去重技术等实际搜索引擎必须关注的技术，同时用相当大的篇幅讲解了云计算与云存储的核心技术原理。另外，本书也密切关注搜索引擎发展的前沿技术：Google的咖啡因系统及Megastore等云计算新技术、百度的暗网抓取技术阿拉丁计划、内容农场作弊、机器学习排序等。诸多新技术在相关章节都有详细讲解，同时对于社会化搜索、实时搜索及情境搜索等搜索引擎的未来发展方向做了技术展望。为了增进读者的理解，全书大量引入形象的图片来讲解算法原理，相信读者会发现原来搜索引擎的核心技术理解起来比原先想象的要简单得多。

人类正在步入与机器共存的科幻世界？看《纽约时报》畅销书作者讲述算法和机器学习技术如何悄然接管人类社会，带我们走进一个算法统治的世界。 今天，算法涉足的领域已经远远超出了其创造者的预期。特别是进入信息时代以后，算法的应用涵盖金融、医疗、法律、体育、娱乐、外交、文化、国家安全等诸多方面，显现出源于人类而又超乎人类的强大威力。本书是《纽约时报》畅销书作者的又一力作，通过一个又一个引人入胜的故事，向读者介绍了算法掌控世界的真实情况，揭示了“机器人革命”是如何悄悄地在我们身边发生的。 本书适合任何对科技史、信息革命、算法原理、数据分析感兴趣的读者。 Christopher Steiner 创业公司Aisle50共同创始人及联席CEO，《福布斯》杂志和《芝加哥论坛报》科技专栏特约作者，《纽约时报》畅销书作者，另著有全美畅销书《油价30元/升》。另外，他常在各种会议就科技、算法、大数据等主题发表演说，阐释算法与大数据如何塑造、变革、掌控人类生活。

封面图片为英国伯明翰塞尔福瑞吉百货大楼，其极具线条感的轮廓外型优美，犹如水波的流动。其外表悬挂了1.5万个铝碟，创造出一种极具现代气息的纹理装饰效果，有如夜空下水流的波光粼粼，闪烁于月光之下，使建筑的商业氛围表现到极致。设计该建筑的英国“未来系统建筑事物所”，将商场内部围合成一个顶部采光的中庭，配以交叉的自动扶梯，使购物环境呈现出一种凝聚的向心力和商业广告的展示效应。作为英国第二商业城市伯明翰的建筑地标，人们称该建筑为“未来的百货商店”。因其设计构思的前卫性，该建筑获得2004年英国皇家建筑学会的“建筑设计奖”和2004年“英国皇家工艺美术委员会奖”等多个奖项。 本书从计算机科学领域的角度出发，介绍了信息检索的基础知识，并对当前信息检索的发展做了回顾，重点介绍了搜索引擎的核心技术，如文档分类和文档聚类问题，以及机器学习和数值计算方法。书中所有重要的思想都用示例进行了解释，生动形象，引人入胜，实现了理论与实战的完美结合。 本书的三位作者均是信息检索领域的顶级专家，两位来自学术教育界，一位来自硅谷业界，使本书既具备深厚的理论基础，又代表了尖端科技水准。因此，该书甫一出版，即被奉为该领域的权威著作，备受瞩目。目前已被众多世界名校采用为信息检索课程的教材。

For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.

From the Back Cover This multivolume work on the analysis of algorithms has long been recognized as the definitive description of classical computer science. The three complete volumes published to date already comprise a unique and invaluable resource in programming theory and practice. Countless readers have spoken about the profound personal influence of Knuth's writings. Scientists have marveled at the beauty and elegance of his analysis, while practicing programmers have successfully applied his "cookbook" solutions to their day-to-day problems. All have admired Knuth for the breadth, clarity, accuracy, and good humor found in his books. To begin the fourth and later volumes of the set, and to update parts of the existing three, Knuth has created a series of small books called fascicles, which will be published t regular intervals. Each fascicle will encompass a section or more of wholly new or evised material. Ultimately, the content of these fascicles will be rolled up into the comprehensive, final versions of each volume, and the enormous undertaking that began in 1962 will be complete. Volume 4, Fascicle 2 This fascicle inaugurates the eagerly awaited publication of Knuth's The Art of Computer Programming, Volume 4: Combinatorial Algorithms. Part of what will be a long chapter on combinatorial searching, the fascicle begins his treatment of how to generate all possibilities. Specifically, it discusses the generation of all n-tuples, then extends those ideas to all permutations. Such algorithms provide a natural motivation by means of which many of the key ideas of combinatorial mathematics can be introduced and explored. In this and other fascicles of Volume 4, Knuth illuminates important theories by discussing related games and puzzles. Even serious programming can be fun.

这本最新的权威著作致力于改变这种状况，作者全面阐述了多处理器编程的指导原则，介绍了编制高效的多处理器程序所必备的算法技术。了解本书所涵盖的多处理器编程关键问题将使在校学生以及相关技术人员受益匪浅。

想要探寻搜索排名、产品推荐、社会化书签和在线匹配背后的力量吗？这本颇具魅力的书籍向你展现如何创建Web 2.0应用程序，从参与性？Internet应用程序产生的大量数据中挖掘金矿。运用本书中介绍的先进算法，你可以编写聪明的程序，以访问其他网站那些有趣的数据集，从自有应用程序的用户中收集数据，或者分析和理解你所发现的数据。 《集体智慧编程》将你带入机器学习和统计的世界，并且阐释了如何从你和他人每天收集的信息中获得关于用户体验、市场营销、个性品味及人类行为的结论。每个算法的描述都十分简明清晰，相关代码均可以立即用于你的网站、博客、Wiki或特定应用程序。本书讲解了下列主题： 可以让在线零售商推荐产品或媒体的协作过滤技术 用于在大数据集中发现同类项组的聚类方法 从数以百万计可能方案中选择问题最佳解决方案的最优化算法 贝叶斯过滤，用在基于单词类型和其他特征的垃圾信息过滤中 支持向量（support-vector）机器，用于在线交友网站中的速配 用于问题解决的演化智能——计算机如何通过多次玩同样的游戏，改进自身代码并获得技能提升 每一章都包含了相关练习，可通过扩展使算法变得更强大。超越简单的数据库支持应用程序模式，让 Internet数据财富为你所用。

The book is Volume 4A, because Volume 4 has itself become a multivolume undertaking. Combinatorial searching is a rich and important topic, and Knuth has too much to say about it that is new, interesting, and useful to fit into a single volume, or two, or maybe even three. This book alone includes approximately 1500 exercises, with answers for self-study, plus hundreds of useful facts that cannot be found in any other publication. Volume 4A surely belongs beside the first three volumes of this classic work in every serious programmer’s library.

《计算机程序设计艺术:半数值算法(第2卷)(英文版)(第3版)》主要内容：关于算法分析的这多卷论著已经长期被公认为经典计算机科学的定义性描述。迄今已出版的完整的三卷已经组成了程序设计理论和实践的惟一的珍贵资源，无数读者都赞扬Knuth的著作对个人的深远影响，科学家们为他的分析的美丽和优雅所惊叹，而从事实践的程序员已经成功地将他的“菜谱式”的解应用到日常问题上，所有人都由于Knuth在书中表现出的博学、清晰、精确和高度幽默而对他无比敬仰。 第2卷为半数值算法，分“随机数”和“算术”两章。本卷总结了主要算法范例及这些算法的基本理论，广泛剖析了计算机程序设计与数值分析间的相互联系。

The leading reference on probabilistic methods in combinatorics-now expanded and updated When it was first published in 1991, The Probabilistic Method became instantly the standard reference on one of the most powerful and widely used tools in combinatorics. Still without competition nearly a decade later, this new edition brings you up to speed on recent developments, while adding useful exercises and over 30ew material. It continues to emphasize the basic elements of the methodology, discussing in a remarkably clear and informal style both algorithmic and classical methods as well as modern applications. The Probabilistic Method, Second Edition begins with basic techniques that use expectation and variance, as well as the more recent martingales and correlation inequalities, then explores areas where probabilistic techniques proved successful, including discrepancy and random graphs as well as cutting-edge topics in theoretical computer science. A series of proofs, or "probabilistic lenses," are interspersed throughout the book, offering added insight into the application of the probabilistic approach. New and revised coverage includes: * Several improved as well as new results * A continuous approach to discrete probabilistic problems * Talagrand's Inequality and other novel concentration results * A discussion of the connection between discrepancy and VC-dimension * Several combinatorial applications of the entropy function and its properties * A new section on the life and work of Paul Erdös-the developer of the probabilistic method

本书在介绍算法时，重点介绍用干设计算法的策略．非常与众不同。书中介绍了剪枝搜索、分摊分析、随机算法、在线算法以及多项式近似方案等相对较新的思想和众多基于分摊分析新开发的算法，每个算法都与实例一起加以介绍，而且每个例子都利用图进行详细解释。此外，本书还提供了超过400幅图来帮助初学者理解。本书适合作为高等院校算法设计与分析课程的高年级本科生和低年级研究生的教材，也可供相美科技人员和专业人七参考使用。

Describes basic programming principles and their step-by- step applications.Numerous examples are included.

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: * Collaborative filtering techniques that enable online retailers to recommend products or media * Methods of clustering to detect groups of similar items in a large dataset * Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm * Optimization algorithms that search millions of possible solutions to a problem and choose the best one * Bayesian filtering, used in spam filters for classifying documents based on word types and other features * Using decision trees not only to make predictions, but to model the way decisions are made * Predicting numerical values rather than classifications to build price models * Support vector machines to match people in online dating sites * Non-negative matrix factorization to find the independent features in a dataset * Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

本书是一部设计与分析领域的经典著作，着重介绍了计算机算法设计领域的基本原则和根本原理。书中深入分析了一些计算机模型上的算法，介绍了一些和设计有效算法有关的数据结构和编程技术，为读者提供了有关递归方法、分治方法和动态规划方面的详细实例和实际应用，并致力于更有效算法的设计和开发。同时，对NP完全等问题能否有效求解进行了分析，并探索了应用启发式算法解决问题的途径。另外，本书还提供了大量富有指导意义的习题。 本书可以作为高等院校计算机算法设计与分析课程的本科生或研究生教材，也可以作为计算机理论研究人员、计算机算法设计人员的参考书。

Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age.

It might seem completely dated with all its examples written in the now outmoded Pascal programming language (well, unless you are one of those Delphi zealot trying to resist to the Java/.NET dominance), but it is still highly recommended. It is the single book I learnt most from when I was a freshman studying Computer Science at my local University (when Pascal was already declining, I'm not so old ;-). I wish more recent books were as good as this one, but I have not found a single book so focused and, at the same time, so broad in scope as this one. Wirth covers programming fundamentals (including recursion), many sorting algorithms, data structures (from simple data collections to B-trees and hashing), and basic compiler technology. That is, four books into one (and, surprisingly, this is not a hefty tome.)