Algorithms and data structures Books

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Book SynopsisAt last, the VB.NET programmer has a dedicated reference - no more translating elementary material from C++ or Java. This is the only VB.NET book that provides comprehensive discussions of the major data structures and algorithms from the .NET Framework Class Library, as well as those that the programmer must develop.Trade Review“Choosing which data structure and sorting algorithms to use can have a great effect on the speed of the program. This book helps programmers make those choices. This book begins with an introduction to properties and classes in VB.NET. It also describes the creation of a timing test in the VB.NET environment, which is used repeatedly in later chapters to demonstrate how different structures and searching techniques can change program completion time. This little bit of code is the prize inside, since it can be used whenever timing of VB.NET programming is needed…[This book] thoroughly covers the basics, and some more advanced topics of data structures and searching algorithms, using VB.NET with a minimalist approach.” Computing ReviewsTable of Contents1. Collections; 2. Arrays and the array class; 3. The arraylist and sortedlist classes; 4. Basic sorting algorithms; 5. Basic searching algorithms; 6. Stacks and queues; 7. BitArrays and the BitVector structure; 8. Strings, the string class and the StringBuilder class; 9. Special string classes - StringCollection, StringDictionary and StringEnumerator; 10. Pattern matching and text processing - using the RegEx and supporting classes; 11. Hash tables; 12. Dictionaries - the DictionaryBase class and specialized dictionary classes; 13. Linked lists; 14. Binary trees and binary search trees; 15. Sets; 16. Advanced sorting algorithms; 17. Advanced searching algorithms; 18. Graphs and graph algorithms; 19. Greedy algorithms; 20. Probabilistic algorithms; 21. Dynamic programming.

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Book SynopsisThis textbook offers a unified and self-contained introduction to the field of term rewriting. It covers all the basic material (abstract reduction systems, termination, confluence, completion, and combination problems), but also some important and closely connected subjects: universal algebra, unification theory, GrÃbner bases and Buchberger's algorithm. The main algorithms are presented both informally and as programs in the functional language Standard ML (an appendix contains a quick and easy introduction to ML). Certain crucial algorithms like unification and congruence closure are covered in more depth and Pascal programs are developed. The book contains many examples and over 170 exercises. This text is also an ideal reference book for professional researchers: results that have been spread over many conference and journal articles are collected together in a unified notation, proofs of almost all theorems are provided, and each chapter closes with a guide to the literature.Trade Review'… a welcome and important addition to the library of any researcher interested in theoretical computer science. It provides a thorough grounding in the subject in a clear style, and gives plenty of indications of further directions, including an extensive bibliography'. The Computer Journal'… a well-balanced textbook … presenting the subject in a unified and systematic manner.' H. Herre, Zentralblatt MATH'… a highly welcome addition to the literature on term rewriting … It is very readable, well written and likeable book. it should be of great value to students and researchers alike.' Jan Willem Klop, Journal of Functioning ProgrammingTable of ContentsPreface; 1. Motivating examples; 2. Abstract reduction systems; 3. Universal algebra; 4. Equational problems; 5. Termination; 6. Confluence; 7. Completion; 8. Gröbner bases and Buchberger's algorithm; 9. Combination problems; 10. Equational unification; 11. Extensions; Appendix 1. Ordered sets; Appendix 2. A bluffer's guide to ML; Bibliography; Index.

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Book SynopsisMore than 40 of the top researchers in this field have written chapters that go from the foundations to the state of the art. Basic chapters on algorithmic methods for equilibria, mechanism design and combinatorial auctions are followed by chapters on incentives and pricing, cost sharing, information markets and cryptography and security.Trade Review'… a tome to be dipped into by researchers and developers who would want to know more about certain aspects of the field and particular 'state-of-the-art' issues and applications.' KybernetesTable of ContentsIntroduction Noam Nisan, Tim Roughgarden, Éva Tardos and Vijay V. Vazirani; Part I. Computing in Games: 1. Basic solution concepts and computational issues Éva Tardos and Vijay V. Vazirani; 2. Algorithms for equilibria Christos Papadimitriou; 3. Equilibrium computation for games in strategic and extensive form Bernhard von Stengel; 4. Learning, regret minimization and correlated equilibria Avrim Blum and Yishay Mansour; 5. Graphical games Michael J. Kearns; 6. Cryptography and game theory Yevgeniy Dodis and Tal Rabin; 7. Combinatorial algorithms for market equilibria Vijay V. Vazirani; 8. Computation of market equilibria by convex programming Bruno Codenotti and Kasturi Varadarajan; Part II. Algorithmic Mechanism Design: 9. Introduction to mechanism design (for computer scientists) Noam Nisan; 10. Mechanism design without money James Schummer and Rakesh V. Vohra; 11. Combinatorial auctions Noam Nisan and Liad Blumrosen; 12. Computationally efficient approximation mechanisms Ron Lavi; 13. Profit maximization in mechanism design Jason Hartline and Anna Karlin; 14. Distributed algorithmic mechanism design Joan Feigenbaum, Michael Schapira and Scott Shenker; 15. Cost sharing Kamal Jain and Mohammad Mahdian; 16. On-line mechanisms David C. Parkes; Part III. Quantifying the Inefficiency of Equilibria: 17. Introduction to the inefficiency of equilibria Tim Roughgarden and Éva Tardos; 18. Routing games Tim Roughgarden; 19. Inefficiency of equilibria in network formation games Éva Tardos and Tom Wexler; 20. Selfish load-balancing Berthold Vöcking; 21. Efficiency loss and the design of scalable resource allocation mechanisms Ramesh Johari; Part IV. Additional Topics: 22. Incentives and pricing in communication networks Asuman Ozdaglar and R. Srikant; 23. Incentives in peer-to-peer systems John Chuang, Michal Feldman and Moshe Babaioff; 24. Cascading behavior in networks: algorithmic and economic issues Jon Kleinberg; 25. Incentives and information security Ross Anderson, Tyler Moore, Shishir Nagaraja and Andy Ozment; 26. Computational aspects of information markets David M. Pennock and Rahul Sami; 27. Manipulation-resistant reputation systems Eric Friedman, Paul Resnick and Rahul Sami; 28. Sponsored search auctions Sebastien Lahaie, David M. Pennock, Amin Saberi and Rakesh V. Vohra; 29. Algorithmic issues in evolutionary game theory Michael Kearns and Siddharth Suri.

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Book SynopsisThis graduate-level text explains the implementation and analysis of data structures as a specialised topic in applied algorithms. It examines efficient ways to realise query operations and the history of various structures as they are related to basic concepts of data storage.Trade Review'I think this book is well suited as a main or supplemental text in a graduate-level data structures course, not to mention an invaluable desk reference for those interested in implementing the advance structures outlined in this book. This book was a joy to review, and deserves a place on my bookshelf.' SIGACT News'It can be briefly said that the reader will be dealing with an illustration, diagram, and code packed book, that will do it's best not to confuse but to very well explain one of the toughest computer science subjects, and he will be pleasantly surprised to learn many new-age data structures.' Igor Gvero, Software Engineering NotesTable of Contents1. Elementary structures; 2. Search types; 3. Balanced search trees; 4. Tree structures for sets of intervals; 5. Heaps; 6. Union-find and related structures; 7. Data structure transformations; 8. Data structures for strings; 9. Hash tables; 10. Appendix.

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Book SynopsisThis self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty.Trade Review'This graduate-level text gives a thorough, rigorous and up-to-date treatment of the main mathematical tools that have been developed for the analysis and design of machine learning methods. It is ideal for a graduate class, and the exercises at the end of each chapter make it suitable for self-study. An excellent addition to the literature from one of the leading researchers in this area, it is sure to become a classic.' Peter Bartlett, University of California, Berkeley'This book showcases the breadth and depth of mathematical ideas in learning theory. The author has masterfully synthesized techniques from the many disciplines that have contributed to this subject, and presented them in an accessible format that will be appreciated by both newcomers and experts alike. Readers will learn the tools-of-the-trade needed to make sense of the research literature and to express new ideas with clarity and precision.' Daniel Hsu, Columbia University'Tong Zhang shares in this book his deep and broad knowledge of machine learning, writing an impressively comprehensive and up-to-date reference text, providing a rigorous and rather advanced treatment of the most important topics and approaches in the mathematical study of machine learning. As an authoritative reference and introduction, his book will be a great asset to the field.' Robert Schapire, Microsoft Research'This book gives a systematic treatment of the modern mathematical techniques that are commonly used in the design and analysis of machine learning algorithms. Written by a key contributor to the field, it is a unique resource for graduate students and researchers seeking to gain a deep understanding of the theory of machine learning.' Shai Shalev-Shwartz, Hebrew University of JerusalemTable of Contents1. Introduction; 2. Basic probability inequalities for sums of independent random variables; 3. Uniform convergence and generalization analysis; 4. Empirical covering number analysis and symmetrization; 5. Covering number estimates; 6. Rademacher complexity and concentration inequalities; 7. Algorithmic stability analysis; 8. Model selection; 9. Analysis of kernel methods; 10. Additive and sparse models; 11. Analysis of neural networks; 12. Lower bounds and minimax analysis; 13. Probability inequalities for sequential random variables; 14. Basic concepts of online learning; 15. Online aggregation and second order algorithms; 16. Multi-armed bandits; 17. Contextual bandits; 18. Reinforcement learning; A. Basics of convex analysis; B. f-Divergence of probability measures; References; Author index; Subject index.

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Book SynopsisThere are many textbooks on algorithms focusing on big-O notation and basic design principles. This book offers a unique approach to taking the design and analyses to the level of predictable practical efficiency, discussing core and classic algorithmic problems that arise in the development of big data applications, and presenting elegant solutions of increasing sophistication and efficiency. Solutions are analyzed within the classic RAM model, and the more practically significant external-memory model that allows one to perform I/O-complexity evaluations. Chapters cover various data types, including integers, strings, trees, and graphs, algorithmic tools such as sampling, sorting, data compression, and searching in dictionaries and texts, and lastly, recent developments regarding compressed data structures. Algorithmic solutions are accompanied by detailed pseudocode and many running examples, thus enriching the toolboxes of students, researchers, and professionals interested in effeTrade Review'When I joined Google in 2000, algorithmic problems came up every day. Even strong engineers didn't have all the background they needed to design efficient algorithms. Paolo Ferragina's well-written and concise book helps fill that void. A strong software engineer who masters this material will be an asset.' Martin Farach-Colton, Rutgers University'There are plenty of books on Algorithm Design, but few about Algorithm Engineering. This is one of those rare books on algorithms that pays the necessary attention to the more practical aspects of the process, which become crucial when actual performance matters, and which render some theoretically appealing algorithms useless in real life. The author is an authority on this challenging path between theory and practice of algorithms, which aims at both conceptually nontrivial and practically relevant solutions. I hope the readers will find the reading as pleasant and inspiring as I did.' Gonzalo Navarro, University of Chile'Ferragina combines his skills as a coding engineer, an algorithmic mathematician, and a pedagogic innovator to engineer a string of pearls made up of beautiful algorithms. In this, beauty dovetails with computational efficiency. His data structures of Stringomics hold the promise for a better understanding of population of genomes and the history of humanity. It belongs in the library of anyone interested in the beauty of code and the code of beauty.' Bud Mishra, Courant Institute, New York University'There are many textbooks on algorithms focusing on big-O notation and general design principles. This book offers a completely unique aspect of taking the design and analyses to the level of predictable practical efficiency. No sacrifices in generality are made, but rather a convenient formalism is developed around external memory efficiency and parallelism provided by modern computers. The benefits of randomization are elegantly used for obtaining simple algorithms, whose insightful analyses provide the reader with useful tools to be applied to other settings. This book will be invaluable in broadening the computer science curriculum with a course on algorithm engineering.' Veli Makinen, University of HelsinkiTable of Contents1. Prologue; 2. A warm-up!; 3. Random sampling; 4. List ranking; 5. Sorting atomic items; 6. Set intersection; 7. Sorting strings; 8. The dictionary problem; 9. Searching strings by prefix; 10. Searching strings by substring; 11. Integer coding; 12. Statistical coding; 13. Dictionary-based compressors; 14. The burrows-wheeler transform; 15. Compressed data structures; 16. Conclusion.

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Book SynopsisFirst published in 1975, this classic book gives a systematic account of transcendental number theory, that is, the theory of those numbers that cannot be expressed as the roots of algebraic equations having rational coefficients. Their study has developed into a fertile and extensive theory, which continues to see rapid progress today. Expositions are presented of theories relating to linear forms in the logarithms of algebraic numbers, of Schmidt''s generalization of the ThueSiegelRoth theorem, of Shidlovsky''s work on Siegel''s E-functions and of Sprindžuk''s solution to the Mahler conjecture. This edition includes an introduction written by David Masser describing Baker''s achievement, surveying the content of each chapter and explaining the main argument of Baker''s method in broad strokes. A new afterword lists recent developments related to Baker''s work.Trade Review'Baker's book is the book on transcendental numbers. He covers a majority of those areas that have reached definitive results, presents most of the proofs in a complete yet far more compact form than hitherto available, and covers historical and bibliographical matters with great thoroughness and impeccable scholarship. As literature, it compares well with the finest works of Landau, Rademacher, and Titchmarsh.' Kenneth B. Stolarsky, Bulletin of the American Mathematical SocietyTable of ContentsIntroduction David Masser; Preface; 1. The origins; 2. Linear forms in logarithms; 3. Lower bounds for linear forms; 4. Diophantine equations; 5. Class numbers of imaginary quadratic fields; 6. Elliptic functions; 7. Rational approximations to algebraic numbers; 8. Mahler's classification; 9. Metrical theory; 10. The exponential function; 11. The Shiegel–Shidlovsky theorems; 12. Algebraic independence; Bibliography; Original papers; Further publications; New developments; Some Developments since 1990 David Masser; Index.

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Book SynopsisLearn with confidence with this hands-on undergraduate textbook for CS2 courses. Active-learning and real-world projects underpin each chapter, briefly reviewing programming fundamentals then progressing to core data structures and algorithms topics including recursion, lists, stacks, trees, graphs, sorting, and complexity analysis. Creative projects and applications put theoretical concepts into practice, helping students master the fundamentals. Dedicated project chapters supply further programming practice using real-world, interdisciplinary problems which students can showcase in their own online portfolios. Example Interview Questions sections prepare students for job applications. The pedagogy supports self-directed and skills-based learning with over 250 ''Try It Yourself'' boxes, many with solutions provided, and over 500 progressively challenging end-of-chapter questions. Written in a clear and engaging style, this textbook is a complete resource for teaching the fundamental skills that today''s students need. Instructor resources are available online, including a test bank, solutions manual, and sample code.

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Book SynopsisThe second edition of this student-friendly textbook now includes over 150 new exercises, key concept summaries and a chapter on machine learning algorithms. Its approachability and clarity make it ideal as both a main course text or as a supplementary book for students who find other books challenging.

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Book SynopsisPublic key cryptography is a major interdisciplinary subject with many real-world applications. This book has been carefully written to communicate the major ideas and techniques in this subject to a wide readership. With numerous examples and exercises, it is suitable as a textbook for an advanced course or for self-study.Trade Review'… the book gathers the main mathematical topics related to public key cryptography and provides an excellent source of information for both students and researchers interested in the field.' Juan Tena Ayuso, Zentralblatt MATHTable of ContentsPreface; Acknowledgements; 1. Introduction; Part I. Background: 2. Basic algorithmic number theory; 3. Hash functions and MACs; Part II. Algebraic Groups: 4. Preliminary remarks on algebraic groups; 5. Varieties; 6. Tori, LUC and XTR; 7. Curves and divisor class groups; 8. Rational maps on curves and divisors; 9. Elliptic curves; 10. Hyperelliptic curves; Part III. Exponentiation, Factoring and Discrete Logarithms: 11. Basic algorithms for algebraic groups; 12. Primality testing and integer factorisation using algebraic groups; 13. Basic discrete logarithm algorithms; 14. Factoring and discrete logarithms using pseudorandom walks; 15. Factoring and discrete logarithms in subexponential time; Part IV. Lattices: 16. Lattices; 17. Lattice basis reduction; 18. Algorithms for the closest and shortest vector problems; 19. Coppersmith's method and related applications; Part V. Cryptography Related to Discrete Logarithms: 20. The Diffie–Hellman problem and cryptographic applications; 21. The Diffie–Hellman problem; 22. Digital signatures based on discrete logarithms; 23. Public key encryption based on discrete logarithms; Part VI. Cryptography Related to Integer Factorisation: 24. The RSA and Rabin cryptosystems; Part VII. Advanced Topics in Elliptic and Hyperelliptic Curves: 25. Isogenies of elliptic curves; 26. Pairings on elliptic curves; Appendix A. Background mathematics; References; Author index; Subject index.

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Book SynopsisStream processing is a novel distributed computing paradigm that supports the gathering, processing and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.Table of ContentsPart I. Fundamentals: 1. What brought us here?; 2. Introduction to stream processing; Part II. Application Development: 3. Application development - the basics; 4. Application development - data flow programming; 5. Large-scale development - modularity, extensibility, and distribution; 6. Application engineering - debugging and visualization; Part III. System Architecture: 7. Architecture of a stream processing system; 8. InfoSphere streams architecture; Part IV. Application Design and Analytics: 9. Design principles and patterns for stream processing applications; 10. Stream processing and mining algorithms; Part V. Case Studies: 11. End-to-end application examples; Part VI. Closing Notes: 12. Conclusion.

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Book SynopsisThis unique resource now includes coverage of Unix dates, Italian time, the Akan, Icelandic, Saudi Arabian Umm al-Qura, Babylonian, Samaritan, and Nepalese calendars, plus expanded treatments of Islamic and Hebrew calendars. The astronomical functions have been rewritten for more accurate results and include calculations of moonrise and moonset.Trade Review'It retains all the features that made the first edition … such a wonderful resource, while adding much new material … If you are at all interested in time and calendars, this book must find a place on your desk.' Victor J. Katz, Mathematical ReviewsTable of Contents1. Calendar basics; Part I. Arithmetical Calendars: 2. The Gregorian calendar; 3. The Julian calendar; 4. The Coptic and Ethiopic calendars; 5. The ISO calendar; 6. The Icelandic calendar; 7. The Islamic calendar; 8. The Hebrew calendar; 9. The Ecclesiastical calendars; 10. The old Hindu calendars; 11. The Mayan calendars; 12. The Balinese Pawukon calendar; 13. Generic Cyclical calendars; Part II. Astronomical Calendars: 14. Time and astronomy; 15. The Persian calendar; 16. The Bahá'í calendar; 17. The French Revolutionary calendar; 18. Astronomical Lunar calendars; 19. The Chinese calendar; 20. The modern Hindu calendars; 21. The Tibetan calendar; Part III. Appendices: A. Function, parameter, and constant types; B. Cross references; C. Sample data; D. Lisp implementation.

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Book SynopsisGreatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.Trade Review'As randomized methods continue to grow in importance, this textbook provides a rigorous yet accessible introduction to fundamental concepts that need to be widely known. The new chapters in this second edition, about sample size and power laws, make it especially valuable for today's applications.' Donald E. Knuth, Stanford University, California'Of all the courses I have taught at Berkeley, my favorite is the one based on the Mitzenmacher-Upfal book Probability and Computing. Students appreciate the clarity and crispness of the arguments and the relevance of the material to the study of algorithms. The new second edition adds much important material on continuous random variables, entropy, randomness and information, advanced data structures and topics of current interest related to machine learning and the analysis of large data sets.' Richard M. Karp, University of California, Berkeley'The new edition is great. I'm especially excited that the authors have added sections on the normal distribution, learning theory and power laws. This is just what the doctor ordered or, more precisely, what teachers such as myself ordered!' Anna Karlin, University of Washington'By assuming just an elementary introduction to discrete probability and some mathematical maturity, this book does an excellent job of introducing a great variety of topics to the reader. I especially liked the coverage of the Poisson, exponential, and (multi-variate) normal distributions and how they arise naturally, machine learning, Bayesian reasoning, Cuckoo hashing etc. There is a broad range of exercises, including helpful ones on programming to get a feel for the numerics … This connection to practice is unusual and very commendable … Overall, I would highly recommend this book to anyone interested in probabilistic and statistical foundations as applied to computer science, data science, etc. It can be taught at the senior undergraduate or graduate level to students in computer science, electrical engineering, operations research, mathematics, and other such disciplines.' Frederic Green , SIGACT NewsTable of Contents1. Events and probability; 2. Discrete random variables and expectations; 3. Moments and deviations; 4. Chernoff and Hoeffding bounds; 5. Balls, bins, and random graphs; 6. The probabilistic method; 7. Markov chains and random walks; 8. Continuous distributions and the Polsson process; 9. The normal distribution; 10. Entropy, randomness, and information; 11. The Monte Carlo method; 12. Coupling of Markov chains; 13. Martingales; 14. Sample complexity, VC dimension, and Rademacher complexity; 15. Pairwise independence and universal hash functions; 16. Power laws and related distributions; 17. Balanced allocations and cuckoo hashing.

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Book SynopsisThe last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.Table of Contents1. Key developments in algorithmic randomness Johanna N. Y. Franklin and Christopher P. Porter; 2. Algorithmic randomness in ergodic theory Henry Towsner; 3. Algorithmic randomness and constructive/computable measure theory Jason Rute; 4. Algorithmic randomness and layerwise computability Mathieu Hoyrup; 5. Relativization in randomness Johanna N. Y. Franklin; 6. Aspects of Chaitin's Omega George Barmpalias; 7. Biased algorithmic randomness Christopher P. Porter; 8. Higher randomness Benoit Monin; 9. Resource bounded randomness and its applications Donald M. Stull; Index.

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Book SynopsisThis book gives students a quick and accessible introduction to many of the most important concepts in the field of algorithmic game theory. It demonstrates these concepts through case studies in online advertising, wireless spectrum auctions, kidney exchange, and network management.Trade Review'There are several features of this book that make it very well suited both for the classroom and for self-study … if your interest is in understanding how game theory, economics and computer science are cross-pollinating to address challenges of the design of online strategic interactions, this is the book to start with. It is clear, well-organized and makes a compelling introduction to a vibrant field.' David Burke, MAA ReviewsTable of Contents1. Introduction and examples; 2. Mechanism design basics; 3. Myerson's Lemma; 4. Algorithmic mechanism design 34; 5. Revenue-maximizing auctions; 6. Simple near-optimal auctions; 7. Multi-parameter mechanism design; 8. Spectrum auctions; 9. Mechanism design with payment constraints; 10. Kidney exchange and stable matching; 11. Selfish routing and the price of anarchy; 12. Network over-provisioning and atomic selfish routing; 13. Equilibria: definitions, examples, and existence; 14. Robust price-of-anarchy bounds in smooth games; 15. Best-case and strong Nash equilibria; 16. Best-response dynamics; 17. No-regret dynamics; 18. Swap regret and the Minimax theorem; 19. Pure Nash equilibria and PLS-completeness; 20. Mixed Nash equilibria and PPAD-completeness.

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Book SynopsisBest practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by mak

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Book SynopsisShortlisted for the 2018 Baillie Gifford Prize and the 2018 Royal Society Investment Science Book Prize "A beautifully accessible guide.…One of the best books yet written on data and algorithms." —Times (UK)Trade Review"With refreshing simplicity, Fry explains what AI, machine learning and complicated algorithms really mean." -- Guardian"Fascinating and funny. I learned something on every page." -- Tom Chivers - Buzzfeed"An action-packed read during which you will be outraged, provoked, and challenged." -- Cathy O’Neil, author of Weapons of Math Destruction"This short, sharp book on the power and dangers of algorithms offers one of the clearest explanations of a complex subject." -- Financial Times"Hannah Fry is one of the best STEM explainers and popularizers today." -- Forbes"For a reader unfamiliar with the technical aspects of AI, this book offers among the best lay explanations of how algorithms work." -- Science"Hannah Fry makes algorithms sound not only quite interesting but an idea that we must understand better as they dominate more and more of our daily lives in ways we see and in many ways we don’t." -- Amazon Book Review"Mixing mathematics and storytelling, this book asks the big questions about algorithms and humans—and their future together." -- Literary Hub"A well-constructed tour of technology and its discontents?timely, too, given the increasing prominence of AI in our daily lives." -- Kirkus Reviews"A lucid and timely analysis." -- Booklist (starred review)

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Book SynopsisAn exhilarating, elegant memoir and a significant polemic on how computers and algorithms shape our understanding of the world and of who we are   Bitwise is a wondrous ode to the computer lan­guages and codes that captured technologist David Auerbach’s imagination. With a philoso­pher’s sense of inquiry, Auerbach recounts his childhood spent drawing ferns with the pro­gramming language Logo on the Apple IIe, his adventures in early text-based video games, his education as an engineer, and his contribu­tions to instant messaging technology devel­oped for Microsoft and the servers powering Google’s data stores. A lifelong student of the systems that shape our lives—from the psy­chiatric taxonomy of the Diagnostic and Statistical Manual to how Facebook tracks and profiles its users—Auerbach reflects on how he has experienced the algorithms that taxonomize human speech, knowledge, and behavior and that compel us to do the same.  Into this exquisitely crafted, wide-ranging memoir of a life spent with code, Auerbach has woven an eye-opening and searing examina­tion of the inescapable ways in which algo­rithms have both standardized and coarsened our lives. As we engineer ever more intricate technology to translate our experiences and narrow the gap that divides us from the ma­chine, Auerbach argues, we willingly erase our nuances and our idiosyncrasies—precisely the things that make us human.

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Book SynopsisThe essential guide to solving algorithmic and networking problems in commercial computer games, revised and extended Algorithms and Networking for Computer Games, Second Editionis written from the perspective of the computer scientist. Combining algorithmic knowledge and game-related problems, it explores the most common problems encountered in game programing. The first part of the book presents practical algorithms for solving classical topics, such as random numbers, procedural generation, tournaments, group formations and game trees. The authors also focus on how to find a path in, create the terrain of, and make decisions in the game world. The second part introduces networking related problems in computer games, focusing on four key questions: how to hide the inherent communication delay, how to best exploit limited network resources, how to cope with cheating and how to measure the on-line game data. Thoroughly revised, updated, and Trade Review“More than 70 algorithms are presented, covering random numbers, noise in data (a realistic world is full of imperfections), procedural generation, tournaments, game trees, path finding, group movement, decision making, and modelling uncertainty – as well as networking problems, including dealing with cheating. The exercises at the end of each chapter range from simple thought exercises to studying Braben and Bell’s namegeneration algorithm from Elite (1984) … use of pseudocode throughout ensures the book works equally well for C, C++, Java, Python, or even C# programmers.” MagPi, Issue 64, December 2017 Table of ContentsPreface xiii 1 Introduction 1 1.1 Anatomy of Computer Games 4 1.2 Game Development 6 1.2.1 Phases of development 7 1.2.2 Documentation 8 1.2.3 Other considerations 11 1.3 Synthetic Players 12 1.3.1 Humanness 13 1.3.2 Stance 14 1.4 Multiplaying 14 1.5 Interactive Storytelling 15 1.5.1 Approaches 16 1.5.2 Storytelling in games 17 1.6 Outline of the Book 19 1.6.1 Algorithms 20 1.6.2 Networking 20 1.7 Summary 21 Exercises 21 I Algorithms 25 2 Random Numbers 26 2.1 Linear Congruential Method 27 2.1.1 Choice of parameters 30 2.1.2 Testing the randomness 32 2.1.3 Using the generators 33 2.2 Discrete Finite Distributions 36 2.3 Random Shuffling 40 2.4 Summary 44 Exercises 44 3 Noise 49 3.1 Applying Noise 50 3.2 Origin of Noise 51 3.3 Visualization 52 3.4 Interpolation 55 3.4.1 Utility routines for value conversions 56 3.4.2 Interpolation in a single parameter 58 3.4.3 Interpolation in two parameters 61 3.5 Composition of Noise 62 3.6 Periodic Noise 65 3.7 Perlin Noise 68 3.8 Worley Noise 73 3.9 Summary 83 Exercises 83 4 Procedural Generation 88 4.1 Terrain Generation 89 4.2 Maze Algorithms 96 4.2.1 Depth-first algorithm 98 4.2.2 Randomized Kruskal’s algorithm 99 4.2.3 Randomized Prim’s algorithm 101 4.3 L-Systems 101 4.3.1 Examples 103 4.3.2 City generation 105 4.4 Hierarchical Universe Generation 108 4.5 Summary 109 Exercises 111 5 Tournaments 115 5.1 Rank Adjustment Tournaments 118 5.2 Elimination Tournaments 123 5.3 Scoring Tournaments 131 5.4 Summary 135 Exercises 138 6 Game Trees 143 6.1 Minimax 144 6.1.1 Analysis 147 6.1.2 Partial minimax 148 6.2 Alpha-Beta Pruning 152 6.2.1 Analysis 156 6.2.2 Principal variation search 157 6.3 Monte Carlo Tree Search 157 6.4 Games of Chance 166 6.5 Summary 168 Exercises 170 7 Path Finding 177 7.1 Discretization of the Game World 178 7.1.1 Grid 179 7.1.2 Navigation mesh 180 7.2 Finding the Minimum Path 182 7.2.1 Evaluation function 183 7.2.2 Properties 184 7.2.3 Algorithm A* 185 7.3 Realizing the Movement 187 7.4 Summary 189 Exercises 190 8 Group Movement 194 8.1 Flocking 195 8.2 Formations 200 8.2.1 Coordinating formations 200 8.2.2 Behaviour-based steering 204 8.2.3 Fuzzy logic control 205 8.2.4 Mass-spring systems 207 8.3 Summary 208 Exercises 208 9 Decision-Making 211 9.1 Background 211 9.1.1 Levels of decision-making 212 9.1.2 Modelled knowledge 213 9.1.3 Methods 214 9.2 Finite State Machines 218 9.2.1 Computational FSM 221 9.2.2 Mealy and Moore machines 224 9.2.3 Implementation 227 9.2.4 Discussion 228 9.3 Influence Maps 231 9.4 Automated Planning 235 9.5 Summary 237 Exercises 240 10 Modelling Uncertainty 246 10.1 Statistical Reasoning 246 10.1.1 Bayes’ theorem 246 10.1.2 Bayesian networks 248 10.1.3 Dempster–Shafer theory 249 10.2 Fuzzy Sets 252 10.2.1 Membership function 253 10.2.2 Fuzzy operations 255 10.2.3 Defuzzification 255 10.3 Fuzzy Constraint Satisfaction Problem 257 10.3.1 Modelling the criteria as fuzzy sets 259 10.3.2 Weighting the criteria importances 262 10.3.3 Aggregating the criteria 262 10.3.4 Making a decision 263 10.4 Summary 263 Exercises 265 II Networking 268 11 Communication Layers 269 11.1 Physical Platform 270 11.1.1 Resource limitations 271 11.1.2 Transmission techniques and protocols 272 11.2 Logical Platform 274 11.2.1 Communication architecture 274 11.2.2 Data and control architecture 275 11.3 Networked Application 277 11.4 Summary 278 Exercises 278 12 Compensating Resource Limitations 283 12.1 Aspects of Compensation 284 12.1.1 Consistency and responsiveness 284 12.1.2 Scalability 287 12.2 Protocol Optimization 291 12.2.1 Message compression 291 12.2.2 Message aggregation 292 12.3 Dead Reckoning 293 12.3.1 Prediction 293 12.3.2 Convergence 295 12.4 Local Perception Filters 297 12.4.1 Linear temporal contour 301 12.4.2 Adding bullet time to the delays 305 12.5 Synchronized Simulation 307 12.6 Interest Management 308 12.6.1 Aura-based interest management 310 12.6.2 Zone-based interest management 310 12.6.3 Visibility-based interest management 312 12.6.4 Class-based interest management 312 12.7 Compensation by Game Design 314 12.7.1 Short active turns 314 12.7.2 Semi-autonomous avatars 315 12.7.3 Interaction via proxies 316 12.8 Summary 317 Exercises 318 13 Cheating Prevention 321 13.1 Technical Exploitations 322 13.1.1 Packet tampering 323 13.1.2 Look-ahead cheating 324 13.1.3 Cracking and other attacks 330 13.2 Collusion 331 13.2.1 Classification 333 13.2.2 Collusion detection 335 13.3 Rule Violations 337 13.4 Summary 338 Exercises 338 14 Online Metrics 341 14.1 Players 344 14.2 Monetization 345 14.3 Acquisition 347 14.4 Game Session 347 14.5 Summary 348 Exercises 348 A Pseudocode Conventions 351 A.1 Changing the Flow of Control 355 A.1.1 Expressions 355 A.1.2 Control structures 357 A.2 Data Structures 360 A.2.1 Values and entities 360 A.2.2 Data collections 360 A.3 Format of Algorithms 365 A.4 Conversion to Existing Programming Languages 367 B Practical Vectors and Matrices 371 B.1 Points and Vectors 372 B.2 Matrices 381 B.3 Conclusion 387 Bibliography 391 Ludography 408 Index 409

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Book SynopsisA detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 ofMeta-heuristic and Evolutionary Algorithms for Engineering Optimizationprovides an overview of optimization and defines it by presenting examples of optimization problems iTable of ContentsPreface xv About the Authors xvii List of Figures xix 1 Overview of Optimization 1 Summary 1 1.1 Optimization 1 1.1.1 Objective Function 2 1.1.2 Decision Variables 2 1.1.3 Solutions of an Optimization Problem 3 1.1.4 Decision Space 3 1.1.5 Constraints or Restrictions 3 1.1.6 State Variables 3 1.1.7 Local and Global Optima 4 1.1.8 Near-Optimal Solutions 5 1.1.9 Simulation 6 1.2 Examples of the Formulation of Various Engineering Optimization Problems 7 1.2.1 Mechanical Design 7 1.2.2 Structural Design 9 1.2.3 Electrical Engineering Optimization 10 1.2.4 Water Resources Optimization 11 1.2.5 Calibration of Hydrologic Models 13 1.3 Conclusion 15 2 Introduction to Meta-Heuristic and Evolutionary Algorithms 17 Summary 17 2.1 Searching the Decision Space for Optimal Solutions 17 2.2 Definition of Terms of Meta-Heuristic and Evolutionary Algorithms 21 2.2.1 Initial State 21 2.2.2 Iterations 21 2.2.3 Final State 21 2.2.4 Initial Data (Information) 21 2.2.5 Decision Variables 22 2.2.6 State Variables 23 2.2.7 Objective Function 23 2.2.8 Simulation Model 24 2.2.9 Constraints 24 2.2.10 Fitness Function 24 2.3 Principles of Meta-Heuristic and Evolutionary Algorithms 25 2.4 Classification of Meta-Heuristic and Evolutionary Algorithms 27 2.4.1 Nature-Inspired and Non-Nature-Inspired Algorithms 27 2.4.2 Population-Based and Single-Point Search Algorithms 28 2.4.3 Memory-Based and Memory-Less Algorithms 28 2.5 Meta-Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains 28 2.6 Generating Random Values of the Decision Variables 29 2.7 Dealing with Constraints 29 2.7.1 Removal Method 30 2.7.2 Refinement Method 30 2.7.3 Penalty Functions 31 2.8 Fitness Function 33 2.9 Selection of Solutions in Each Iteration 33 2.10 Generating New Solutions 34 2.11 The Best Solution in Each Algorithmic Iteration 35 2.12 Termination Criteria 35 2.13 General Algorithm 36 2.14 Performance Evaluation of Meta-Heuristic and Evolutionary Algorithms 36 2.15 Search Strategies 39 2.16 Conclusion 41 References 41 3 Pattern Search 43 Summary 43 3.1 Introduction 43 3.2 Pattern Search (PS) Fundamentals 44 3.3 Generating an Initial Solution 47 3.4 Generating Trial Solutions 47 3.4.1 Exploratory Move 47 3.4.2 Pattern Move 49 3.5 Updating the Mesh Size 50 3.6 Termination Criteria 50 3.7 User-Defined Parameters of the PS 51 3.8 Pseudocode of the PS 51 3.9 Conclusion 52 References 52 4 Genetic Algorithm 53 Summary 53 4.1 Introduction 53 4.2 Mapping the Genetic Algorithm (GA) to Natural Evolution 54 4.3 Creating an Initial Population 56 4.4 Selection of Parents to Create a New Generation 56 4.4.1 Proportionate Selection 57 4.4.2 Ranking Selection 58 4.4.3 Tournament Selection 59 4.5 Population Diversity and Selective Pressure 59 4.6 Reproduction 59 4.6.1 Crossover 60 4.6.2 Mutation 62 4.7 Termination Criteria 63 4.8 User- Defined Parameters of the GA 63 4.9 Pseudocode of the GA 64 4.10 Conclusion 65 References 65 5 Simulated Annealing 69 Summary 69 5.1 Introduction 69 5.2 Mapping the Simulated Annealing (SA) Algorithm to the Physical Annealing Process 70 5.3 Generating an Initial State 72 5.4 Generating a New State 72 5.5 Acceptance Function 74 5.6 Thermal Equilibrium 75 5.7 Temperature Reduction 75 5.8 Termination Criteria 76 5.9 User- Defined Parameters of the SA 76 5.10 Pseudocode of the SA 77 5.11 Conclusion 77 References 77 6 Tabu Search 79 Summary 79 6.1 Introduction 79 6.2 Tabu Search (TS) Foundation 80 6.3 Generating an Initial Searching Point 82 6.4 Neighboring Points 82 6.5 Tabu Lists 84 6.6 Updating the Tabu List 84 6.7 Attributive Memory 85 6.7.1 Frequency-Based Memory 85 6.7.2 Recency-Based Memory 85 6.8 Aspiration Criteria 87 6.9 Intensification and Diversification Strategies 87 6.10 Termination Criteria 87 6.11 User- Defined Parameters of the TS 87 6.12 Pseudocode of the TS 88 6.13 Conclusion 89 References 89 7 Ant Colony Optimization 91 Summary 91 7.1 Introduction 91 7.2 Mapping Ant Colony Optimization (ACO) to Ants’ Foraging Behavior 92 7.3 Creating an Initial Population 94 7.4 Allocating Pheromone to the Decision Space 96 7.5 Generation of New Solutions 98 7.6 Termination Criteria 99 7.7 User- Defined Parameters of the ACO 99 7.8 Pseudocode of the ACO 100 7.9 Conclusion 100 References 101 8 Particle Swarm Optimization 103 Summary 103 8.1 Introduction 103 8.2 Mapping Particle Swarm Optimization (PSO) to the Social Behavior of Some Animals 104 8.3 Creating an Initial Population of Particles 107 8.4 The Individual and Global Best Positions 107 8.5 Velocities of Particles 109 8.6 Updating the Positions of Particles 110 8.7 Termination Criteria 110 8.8 User- Defined Parameters of the PSO 110 8.9 Pseudocode of the PSO 111 8.10 Conclusion 112 References 112 9 Differential Evolution 115 Summary 115 9.1 Introduction 115 9.2 Differential Evolution (DE) Fundamentals 116 9.3 Creating an Initial Population 118 9.4 Generating Trial Solutions 119 9.4.1 Mutation 119 9.4.2 Crossover 119 9.5 Greedy Criteria 120 9.6 Termination Criteria 120 9.7 User-Defined Parameters of the DE 120 9.8 Pseudocode of the DE 121 9.9 Conclusion 121 References 121 10 Harmony Search 123 Summary 123 10.1 Introduction 123 10.2 Inspiration of the Harmony Search (HS) 124 10.3 Initializing the Harmony Memory 125 10.4 Generating New Harmonies (Solutions) 127 10.4.1 Memory Strategy 127 10.4.2 Random Selection 128 10.4.3 Pitch Adjustment 129 10.5 Updating the Harmony Memory 129 10.6 Termination Criteria 130 10.7 User- Defined Parameters of the HS 130 10.8 Pseudocode of the HS 130 10.9 Conclusion 131 References 131 11 Shuffled Frog-Leaping Algorithm 133 Summary 133 11.1 Introduction 133 11.2 Mapping Memetic Evolution of Frogs to the Shuffled Frog Leaping Algorithm (SFLA) 134 11.3 Creating an Initial Population 137 11.4 Classifying Frogs into Memeplexes 137 11.5 Frog Leaping 138 11.6 Shuffling Process 140 11.7 Termination Criteria 141 11.8 User-Defined Parameters of the SFLA 141 11.9 Pseudocode of the SFLA 141 11.10 Conclusion 142 References 142 12 Honey-Bee Mating Optimization 145 Summary 145 12.1 Introduction 145 12.2 Mapping Honey-Bee Mating Optimization (HBMO) to the Honey- Bee Colony Structure 146 12.3 Creating an Initial Population 148 12.4 The Queen 150 12.5 Drone Selection 150 12.5.1 Mating Flights 151 12.5.2 Trial Solutions 152 12.6 Brood (New Solution) Production 152 12.7 Improving Broods (New Solutions) by Workers 155 12.8 Termination Criteria 156 12.9 User-Defined Parameters of the HBMO 156 12.10 Pseudocode of the HBMO 156 12.11 Conclusion 158 References 158 13 Invasive Weed Optimization 163 Summary 163 13.1 Introduction 163 13.2 Mapping Invasive Weed Optimization (IWO) to Weeds’ Biology 164 13.3 Creating an Initial Population 167 13.4 Reproduction 167 13.5 The Spread of Seeds 168 13.6 Eliminating Weeds with Low Fitness 169 13.7 Termination Criteria 170 13.8 User- Defined Parameters of the IWO 170 13.9 Pseudocode of the IWO 170 13.10 Conclusion 171 References 171 14 Central Force Optimization 175 Summary 175 14.1 Introduction 175 14.2 Mapping Central Force Optimization (CFO) to Newtons Gravitational Law 176 14.3 Initializing the Position of Probes 177 14.4 Calculation of Accelerations 180 14.5 Movement of Probes 181 14.6 Modification of Deviated Probes 181 14.7 Termination Criteria 182 14.8 User-Defined Parameters of the CFO 182 14.9 Pseudocode of the CFO 183 14.10 Conclusion 183 References 183 15 Biogeography-Based Optimization 185 Summary 185 15.1 Introduction 185 15.2 Mapping Biogeography-Based Optimization (BBO) to Biogeography Concepts 186 15.3 Creating an Initial Population 188 15.4 Migration Process 189 15.5 Mutation 191 15.6 Termination Criteria 192 15.7 User- Defined Parameters of the BBO 192 15.8 Pseudocode of the BBO 193 15.9 Conclusion 193 References 194 16 Firefly Algorithm 195 Summary 195 16.1 Introduction 195 16.2 Mapping the Firefly Algorithm (FA) to the Flashing Characteristics of Fireflies 196 16.3 Creating an Initial Population 198 16.4 Attractiveness 199 16.5 Distance and Movement 199 16.6 Termination Criteria 200 16.7 User-Defined Parameters of the FA 200 16.8 Pseudocode of the FA 201 16.9 Conclusion 201 References 201 17 Gravity Search Algorithm 203 Summary 203 17.1 Introduction 203 17.2 Mapping the Gravity Search Algorithm (GSA) to the Law of Gravity 204 17.3 Creating an Initial Population 205 17.4 Evaluation of Particle Masses 207 17.5 UpdatingVelocities and Positions 207 17.6 Updating Newton’s Gravitational Factor 208 17.7 Termination Criteria 209 17.8 User- Defined Parameters of the GSA 209 17.9 Pseudocode of the GSA 209 17.10 Conclusion 210 References 210 18 Bat Algorithm 213 Summary 213 18.1 Introduction 213 18.2 Mapping the Bat Algorithm (BA) to the Behavior of Microbats 214 18.3 Creating an Initial Population 215 18.4 Movement of Virtual Bats 217 18.5 Local Search and Random Flying 218 18.6 Loudness and Pulse Emission 218 18.7 Termination Criteria 219 18.8 User-Defined Parameters of the BA 219 18.9 Pseudocode of the BA 219 18.10 Conclusion 220 References 220 19 Plant Propagation Algorithm 223 Summary 223 19.1 Introduction 223 19.2 Mapping the Natural Process to the Planet Propagation Algorithm (PPA) 223 19.3 Creating an Initial Population of Plants 226 19.4 Normalizing the Fitness Function 226 19.5 Propagation 227 19.6 Elimination of Extra Solutions 228 19.7 Termination Criteria 228 19.8 User-Defined Parameters of the PPA 228 19.9 Pseudocode of the PPA 229 19.10 Conclusion 230 References 230 20 Water Cycle Algorithm 231 Summary 231 20.1 Introduction 231 20.2 Mapping the Water Cycle Algorithm (WCA) to the Water Cycle 232 20.3 Creating an Initial Population 233 20.4 Classification of Raindrops 235 20.5 Streams Flowing to the Rivers or Sea 236 20.6 Evaporation 237 20.7 Raining Process 238 20.8 Termination Criteria 239 20.9 User-Defined Parameters of the WCA 239 20.10 Pseudocode of the WCA 239 20.11 Conclusion 240 References 240 21 Symbiotic Organisms Search 241 Summary 241 21.1 Introduction 241 21.2 Mapping Symbiotic Relations to the Symbiotic Organisms Search (SOS) 241 21.3 Creating an Initial Ecosystem 242 21.4 Mutualism 244 21.5 Commensalism 245 21.6 Parasitism 245 21.7 Termination Criteria 246 21.8 Pseudocode of the SOS 246 21.9 Conclusion 247 References 247 22 Comprehensive Evolutionary Algorithm 249 Summary 249 22.1 Introduction 249 22.2 Fundamentals of the Comprehensive Evolutionary Algorithm (CEA) 250 22.3 Generating an Initial Population of Solutions 253 22.4 Selection 253 22.5 Reproduction 255 22.5.1 Crossover Operators 255 22.5.2 Mutation Operators 261 22.6 Roles of Operators 262 22.7 Input Data to the CEA 263 22.8 Termination Criteria 264 22.9 Pseudocode of the CEA 265 22.10 Conclusion 265 References 266 Wiley Series in Operations Research and Management Science 267 Index 269

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Book SynopsisAn authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors?noted experts on the topic?provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition,Table of ContentsList of Contributors xiii Series Preface xv Preface xvii 1 Metaheuristic Algorithms in Fuzzy Clustering 1Sourav De, Sandip Dey, and Siddhartha Bhattacharyya 1.1 Introduction 1 1.2 Fuzzy Clustering 1 1.2.1 Fuzzy c-means (FCM) clustering 2 1.3 Algorithm 2 1.3.1 Selection of Cluster Centers 3 1.4 Genetic Algorithm 3 1.5 Particle Swarm Optimization 5 1.6 Ant Colony Optimization 6 1.7 Artificial Bee Colony Algorithm 7 1.8 Local Search-Based Metaheuristic Clustering Algorithms 7 1.9 Population-Based Metaheuristic Clustering Algorithms 8 1.9.1 GA-Based Fuzzy Clustering 8 1.9.2 PSO-Based Fuzzy Clustering 9 1.9.3 Ant Colony Optimization–Based Fuzzy Clustering 10 1.9.4 Artificial Bee Colony Optimization–Based Fuzzy Clustering 10 1.9.5 Differential Evolution–Based Fuzzy Clustering 11 1.9.6 Firefly Algorithm–Based Fuzzy Clustering 12 1.10 Conclusion 13 References 13 2 Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications 19Laith Mohammad Abualigah, Mofleh Al-diabat, Mohammad Al Shinwan, Khaldoon Dhou, Bisan Alsalibi, Essam Said Hanandeh, and Mohammad Shehab 2.1 Introduction 19 2.2 Research Framework 21 2.3 Text Preprocessing 22 2.3.1 Tokenization 22 2.3.2 StopWords Removal 22 2.3.3 Stemming 23 2.3.4 Text Document Representation 23 2.3.5 TermWeight (TF-IDF) 23 2.4 Text Feature Selection 24 2.4.1 Mathematical Model of the Feature Selection Problem 24 2.4.2 Solution Representation 24 2.4.3 Fitness Function 24 2.5 Harmony Search Algorithm 25 2.5.1 Parameters Initialization 25 2.5.2 Harmony Memory Initialization 26 2.5.3 Generating a New Solution 26 2.5.4 Update Harmony Memory 27 2.5.5 Check the Stopping Criterion 27 2.6 Text Clustering 27 2.6.1 Mathematical Model of the Text Clustering 27 2.6.2 Find Clusters Centroid 27 2.6.3 Similarity Measure 28 2.7 k-means text clustering algorithm 28 2.8 Experimental Results 29 2.8.1 Evaluation Measures 29 2.8.1.1 F-measure Based on Clustering Evaluation 30 2.8.1.2 Accuracy Based on Clustering Evaluation 31 2.8.2 Results and Discussions 31 2.9 Conclusion 34 References 34 3 Adaptive Position–Based Crossover in the Genetic Algorithm for Data Clustering 39Arnab Gain and Prasenjit Dey 3.1 Introduction 39 3.2 Preliminaries 40 3.2.1 Clustering 40 3.2.1.1 k-means Clustering 40 3.2.2 Genetic Algorithm 41 3.3 RelatedWorks 42 3.3.1 GA-Based Data Clustering by Binary Encoding 42 3.3.2 GA-Based Data Clustering by Real Encoding 43 3.3.3 GA-Based Data Clustering for Imbalanced Datasets 44 3.4 Proposed Model 44 3.5 Experimentation 46 3.5.1 Experimental Settings 46 3.5.2 DB Index 47 3.5.3 Experimental Results 49 3.6 Conclusion 51 References 57 4 Application of Machine Learning in the Social Network 61Belfin R. V., E. Grace Mary Kanaga, and Suman Kundu 4.1 Introduction 61 4.1.1 Social Media 61 4.1.2 Big Data 62 4.1.3 Machine Learning 62 4.1.4 Natural Language Processing (NLP) 63 4.1.5 Social Network Analysis 64 4.2 Application of Classification Models in Social Networks 64 4.2.1 Spam Content Detection 65 4.2.2 Topic Modeling and Labeling 65 4.2.3 Human Behavior Analysis 67 4.2.4 Sentiment Analysis 68 4.3 Application of Clustering Models in Social Networks 68 4.3.1 Recommender Systems 69 4.3.2 Sentiment Analysis 70 4.3.3 Information Spreading or Promotion 70 4.3.4 Geolocation-Specific Applications 70 4.4 Application of Regression Models in Social Networks 71 4.4.1 Social Network and Human Behavior 71 4.4.2 Emotion Contagion through Social Networks 73 4.4.3 Recommender Systems in Social Networks 74 4.5 Application of Evolutionary Computing and Deep Learning in Social Networks 74 4.5.1 Evolutionary Computing and Social Network 75 4.5.2 Deep Learning and Social Networks 75 4.6 Summary 76 Acknowledgments 77 References 78 5 Predicting Students’ Grades Using CART, ID3, and Multiclass SVM Optimized by the Genetic Algorithm (GA): A Case Study 85Debanjan Konar, Ruchita Pradhan, Tania Dey, Tejaswini Sapkota, and Prativa Rai 5.1 Introduction 85 5.2 Literature Review 87 5.3 Decision Tree Algorithms: ID3 and CART 88 5.4 Multiclass Support Vector Machines (SVMs) Optimized by the Genetic Algorithm (GA) 90 5.4.1 Genetic Algorithms for SVM Model Selection 92 5.5 Preparation of Datasets 93 5.6 Experimental Results and Discussions 95 5.7 Conclusion 96 References 96 6 Cluster Analysis of Health Care Data Using Hybrid Nature-Inspired Algorithms 101Kauser Ahmed P, Rishabh Agrawal 6.1 Introduction 101 6.2 RelatedWork 102 6.2.1 Firefly Algorithm 102 6.2.2 k-means Algorithm 103 6.3 Proposed Methodology 104 6.4 Results and Discussion 106 6.5 Conclusion 110 References 111 7 Performance Analysis Through a Metaheuristic Knowledge Engine 113Indu Chhabra and Gunmala Suri 7.1 Introduction 113 7.2 Data Mining and Metaheuristics 114 7.3 Problem Description 115 7.4 Association Rule Learning 116 7.4.1 Association Mining Issues 116 7.4.2 Research Initiatives and Projects 116 7.5 Literature Review 117 7.6 Methodology 119 7.6.1 Phase 1: Pattern Search 120 7.6.2 Phase 2: Rule Mining 120 7.6.3 Phase 3: Knowledge Derivation 121 7.7 Implementation 121 7.7.1 Test Issues 121 7.7.2 System Evaluation 121 7.7.2.1 Indicator Matrix Formulation 122 7.7.2.2 Phase 1: Frequent Pattern Derivation 123 7.7.2.3 Phase 2: Association Rule Framing 123 7.7.2.4 Phase 3: Knowledge Discovery Through Metaheuristic Implementation 123 7.8 Performance Analysis 124 7.9 Research Contributions and Future Work 125 7.10 Conclusion 126 References 126 8 Magnetic Resonance Image Segmentation Using a Quantum-Inspired Modified Genetic Algorithm (QIANA) Based on FRCM 129Sunanda Das, Sourav De, Sandip Dey, and Siddhartha Bhattacharyya 8.1 Introduction 129 8.2 Literature Survey 131 8.3 Quantum Computing 133 8.3.1 Quoit-Quantum Bit 133 8.3.2 Entanglement 133 8.3.3 Measurement 133 8.3.4 Quantum Gate 134 8.4 Some Quality Evaluation Indices for Image Segmentation 134 8.4.1 F(I) 134 8.4.2 F’(I) 135 8.4.3 Q(I) 135 8.5 Quantum-Inspired Modified Genetic Algorithm (QIANA)–Based FRCM 135 8.5.1 Quantum-Inspired MEGA (QIANA)–Based FRCM 136 8.6 Experimental Results and Discussion 139 8.7 Conclusion 147 References 147 9 A Hybrid Approach Using the k-means and Genetic Algorithms for Image Color Quantization 151Marcos Roberto e Souza, Anderson Carlos Sousa e Santos, and Helio Pedrini 9.1 Introduction 151 9.2 Background 152 9.3 Color Quantization Methodology 154 9.3.1 Crossover Operators 157 9.3.2 Mutation Operators 158 9.3.3 Fitness Function 158 9.4 Results and Discussions 159 9.5 Conclusions and Future Work 168 Acknowledgments 168 References 168 Index 173

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Book SynopsisMETA-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systemsgenerally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed system

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Book SynopsisThis book combines the research activities of the authors, both of whom are researchers at Ecole Nationale de l’Aviation Civile (French National School of Civil Aviation), and presents their findings from the last 15 years. Their work uses air transport as its focal point, within the realm of mathematical optimization, looking at real life problems and theoretical models in tandem, and the challenges that accompany studying both approaches. The authors’ research is linked with the attempt to reduce air space congestion in Western Europe, USA and, increasingly, Asia. They do this through studying stochastic optimization (particularly artificial evolution), the sectorization of airspace, route distribution and takeoff slots, and by modeling airspace congestion. Finally, the authors discuss their short, medium and long term research goals. They hope that their work, although related to air transport, will be applied to other fields, such is the transferable nature of mathematical optimization. At the same time, they intend to use other areas of research, such as approximation and statistics to complement their continued inquiry in their own field. Contents 1. Introduction. Part 1. Optimization and Artificial Evolution 2. Optimization: State of the Art. 3. Genetic Algorithms and Improvements. 4. A new concept for Genetic Algorithms based on Order Statistics. Part 2. Applications to Air Traffic Control 5. Air Traffic Control. 6. Contributions to Airspace Sectorization. 7. Contribution to Traffic Assignment. 8. Airspace Congestion Metrics. 9. Conclusion and Future Perspectives. About the Authors Daniel Delahaye works for Ecole Nationale de l’Aviation Civile (French National School of Civil Aviation) in France. Stéphane Puechmorel works for Ecole Nationale de l’Aviation Civile (French National School of Civil Aviation) in France.Table of ContentsIntroduction xi PART 1. OPTIMIZATION AND ARTIFICIAL EVOLUTION 1 Chapter 1. Optimization: State of the Art 3 1.1. Methodological principles in optimization 3 1.1.1. Introduction 3 1.1.2. Modeling 4 1.1.3. Complexity 12 1.1.4. Computation time 13 1.1.5. Conclusion 13 1.2. Optimization algorithms 14 1.2.1. Introduction 14 1.2.2. Linear programming 15 1.2.3. Nonlinear programming (NLP) 16 1.2.4. Local methods subject to constraints 19 1.2.5. Deterministic global methods 21 1.2.6. Stochastic global methods 25 1.2.7. Genetic algorithms 33 1.2.8. Conclusion 34 Chapter 2. Genetic Algorithms and Improvements 37 2.1. General points 37 2.1.1. Introduction 37 2.1.2. Principle of genetic algorithms 39 2.1.3. Coding principles 42 2.1.4. Random generation of the initial population 42 2.1.5. Crossover operators 43 2.1.6. Mutation operators 45 2.1.7. Selection principles 47 2.2. Classic improvements 48 2.2.1. Scaling 49 2.2.2. Sharing 50 2.2.3. Crowding 52 2.2.4. Memetic algorithms 53 2.2.5. Multi-objective genetic algorithms 53 2.3. Our contributions 57 2.3.1. Adaptive clustered sharing 58 2.3.2. Association of genetic algorithms with simulated annealing 60 2.3.3. Parallel genetic algorithms 64 2.4. Conclusion 66 Chapter 3. A New Concept for Genetic Algorithms Based on Order Statistics 67 3.1. Introduction 67 3.2. Order statistics 68 3.3. Estimating the probability that the global optimum belongs to a given domain 71 3.4. Genetic algorithms and order statistics 71 3.4.1. Introduction 71 3.4.2. Coding 72 3.4.3. Recombination operators 73 3.4.4. Evaluation of fitness 75 3.5. Application to test functions 75 3.5.1. Results for the Griewank function 77 3.5.2. Results for the Rosenbrook function 78 3.5.3. Results for the Lennard-Jones function 79 3.6. Conclusion 81 PART 2. APPLICATIONS TO AIR TRAFFIC CONTROL 83 Chapter 4. Air Traffic Control 85 Chapter 5. Contributions to Airspace Sectorization 91 5.1. Introduction 91 5.2. Modeling in 2D 93 5.2.1. Model based on a transportation network 93 5.2.2. Associated complexity 98 5.3. Continuous modeling 99 5.3.1. Principle 99 5.3.2. Chromosome coding 101 5.3.3. Initial population generation principle 101 5.3.4. Crossover operator 101 5.3.5. Mutation operator 103 5.3.6. Calculation and normalization of the fitness function 104 5.3.7. Results 106 5.3.8. Conclusion 110 5.4. Discrete modeling 111 5.4.1. Principle 111 5.4.2. Coding 113 5.4.3. Recombination operators 115 5.4.4. Results 117 5.4.5. Conclusion 119 5.5. Extension 3D 119 5.5.1. Introduction 119 5.5.2. Mathematical modeling 122 5.5.3. Application of artificial evolution to the problem 127 5.5.4. Results 132 5.5.5. Conclusion 135 5.6. Accounting for the dynamic aspect 136 5.6.1. Formalization of objectives and associated mathematical model 136 5.6.2. Optimization using a genetic algorithm: continuous approach 140 5.6.3. Optimization using a genetic algorithm: discrete approach 144 Chapter 6. Contribution to Traffic Assignment 151 6.1. Summary of traffic assignment methods based on transportation network theory 152 6.1.1. Transportation networks 153 6.1.2. Static assignment 155 6.1.3. Dynamic assignment 163 6.2. Other approaches to traffic assignment 167 6.2.1. Temporal extension of the network 167 6.2.2. Optimal control 168 6.2.3. Dynamic programming approaches (ground holding problem) 169 6.2.4. Conclusion 171 6.3. Using artificial evolution in all-or-nothing traffic assignment 173 6.3.1. Mathematical formalization of objectives 173 6.3.2. Coding and operators of the genetic algorithm 176 6.3.3. Introduction of an inter-chromosome distance for sharing 179 6.3.4. Example of results 182 6.3.5. Conclusion 185 6.4. Allocation of routes and slots using artificial evolution 186 6.4.1. System architecture 187 6.4.2. The fitness function 192 6.4.3. Simple genetic algorithm 194 6.5. Modification of the algorithm – adaptive modifications 198 6.5.1. Establishing congestion levels in the chromosome 198 6.5.2. Establishment of trends 200 6.5.3. New coding and biased initial population 203 6.5.4. New crossover operator 203 6.5.5. New mutation operator 204 6.5.6. New results 205 6.5.7. Dynamic bi-allocation 207 6.5.8. Multi-objective approach 210 6.5.9. Conclusion 211 6.6. Sequencing flights for landing 211 6.6.1. Introduction 212 6.6.2. Single runway formulation 213 6.6.3. Modeling using GA 214 6.6.4. Results 217 6.7. Trajectory planning 220 6.7.1. Introduction 220 6.7.2. The light propagation algorithm 222 6.7.3. Approach using genetic algorithms on B-splines 234 6.8. Conclusion 241 Chapter 7. Airspace Congestion Metrics 243 7.1. Introduction 243 7.2. Flow-based approach 248 7.2.1. Mathematical modeling of the control workload 253 7.3. Geometrical approaches 253 7.3.1. Proximity metric 254 7.3.2. Convergence 258 7.3.3. Clusters 263 7.3.4. Grassmannian indicator 265 7.4. Approach based on dynamic systems 268 7.4.1. Linear dynamic systems 268 7.4.2. Spatial extension using nonlinear dynamic systems 273 7.4.3. Spatiotemporal extension using nonlinear dynamic systems 281 7.4.4. Local linear models 285 7.4.5. Stochastic extension 288 Conclusion and Future Perspectives 291 Bibliography 299 Index 327

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Book SynopsisData science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Author Stylianos Kampakis introduces you to the critical aspect of ethics in AI, an area of growing importance and scrutiny. The narrative examines the ethical considerations intrinsic to the development and deployment of AI technologies, including bias, fairness, transparency, and accountability. You'll be provided with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated edition also includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists. Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization.The Decision Maker's Handbook to Data Sciencebridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will LearnIntegrate AI with other innovative technologies Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data scienceDiscover how to hire and manage data scientistsBuild the right environment in order to make your organization data-drivenWho This Book Is ForStartup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.

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