General Books - Page 6

Introduction to Evolutionary Computing (Natural Computing Series)

A.E. Eiben, J.E. Smith

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Customer Reviews:
Total reviews: 6 Average rating: 4.5 of 5

Excellent introduction 5 out of 5 stars.
20 of 21 people found this review helpful.

I taught our introduction to evolutionary computation class from this book. It is a well rounded introduction to the topic covering most of the introductorty material you would expect. There is an real dearth of good introductory books for EC. This is probably the best because of its breadth. Its weakness is its lack of detail. It would not hurt if they covered the same material in about 50% more pages. As soon as they start a topic its over and on to the next topic. But if you are new to the field they give plenty of references and touch on most topics in enough detail for students to implement. All in all a good solid job.

Editorial Review:

Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a variety of problems, ranging from practical applications in industry and commerce to leading-edge scientific research. This book presents the first complete overview of this exciting field aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used rather than just studied. Last, but not least, this book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

An Introduction to Genetic Algorithms (Complex Adaptive Systems)

Melanie Mitchell

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Customer Reviews:
Total reviews: 17 Average rating: 4.0 of 5

Editorial Review:

"An outstanding introduction to a new and important field of computer science." -- Tim Watson, The Computer Journal

"This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing." -- Chris Robbins, Computing

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.

Probability and Computing: Randomized Algorithms and Probabilistic Analysis

Michael Mitzenmacher, Eli Upfal

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Customer Reviews:
Total reviews: 5 Average rating: 3.5 of 5

Another poorly written text book 1 out of 5 stars.
16 of 39 people found this review helpful.

The authors must be smart guys. They obviously understand alot about this subject but make the mistake that you do too! As a result, the book is inadequate as a teaching tool.

They use only half to a third of the narrative they need to adequately explain a subject. They also like to leave out proof steps or not explain them. The problems at the end of chapters are poor as well, since the authors seem to have forgotten to teach the techniques needed to solve most them in the chapter they belong to.

I am sure to them it is intuitive.

Editorial Review:

Assuming only an elementary background in discrete mathematics, this textbook is an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It includes random sampling, expectations, Markov's and Chevyshev's inequalities, Chernoff bounds, balls and bins models, the probabilistic method, Markov chains, MCMC, martingales, entropy, and other topics. The book is designed to accompany a one- or two-semester course for graduate students in computer science and applied mathematics.

Data Structures and Algorithm Analysis in Java (2nd Edition)

Mark Allen Weiss

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Customer Reviews:
Total reviews: 17 Average rating: 3.0 of 5

Editorial Review:

As the speed and power of computers increases, so does the need for effective programming and algorithm analysis. By approaching these skills in tandem, Mark Allen Weiss teaches readers to develop well-constructed, maximally efficient programs in Java. A full language update to Java 5.0 throughout the text--particularly its use of generics-adds immeasurable value to this advanced study of data structures and algorithms. This Second Edition features integrated coverage of the Java Collections Library as well as a complete revision of lists, stacks, queues, and trees. Weiss clearly explains topics from binary heaps to sorting to NP-completeness, and dedicates a full chapter to amortized analysis and advanced data structures and their implementation. Figures and examples illustrating successive stages of algorithms contribute to Weiss' careful, rigorous and in-depth analysis of each type of algorithm. A logical organization of topics and full access to source code compliment the text's coverage.

Graph Theory and Its Applications

Jay Yellen, Jonathan L. Gross

List Price: $89.95 By: CRC Press Amazon Marketplace: 20 new & used starting at $19.95

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Customer Reviews:
Total reviews: 7 Average rating: 4.0 of 5

great, comprehensive introduction 5 out of 5 stars.
8 of 10 people found this review helpful.

Regardless of whether you just want to implement a couple of graph algorithms or get into the guts of graph theoretic proofs, this book should come in as a great resource.

In over 500 pages, this book covers a lot of ground beyond the basics, such as topology of graphs, graph operations and mappings, voltage graphs, and surface imbeddings. Definitions are very clear, propositions and proofs are stated very clearly, and there are shrink-wrapped algorithms if you just want to apply them.

Requiring no previous knowledge of abstract algebra or graph theory, this is a great resource to have in your bookshelf.

not recommended 2 out of 5 stars.
5 of 20 people found this review helpful.

This book was used for my undergraduate course in introductory graph theory, which was split between math and computer science students. I found that this book left to be desired. The definitions are imprecise and often inconsistent with those that are standard, and much of the notation used is not standard. I would not recommend this book as a reference or for advanced students.

Editorial Review:

This applications-driven textbook provides material for an introductory course in graph theory.

An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)

Neil C. Jones, Pavel A. Pevzner

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Customer Reviews:
Total reviews: 7 Average rating: 4.5 of 5

Editorial Review:

This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems.

The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively.

An Introduction to Bioinformatics Algorithms is one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable.

PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Authors' website.

Algorithmic Diagnosis of Symptoms and Signs: Cost-Effective Approach

R. Douglas Collins

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Customer Reviews:
Total reviews: 3 Average rating: 4.5 of 5

Good for NP students too 5 out of 5 stars.
5 of 5 people found this review helpful.

I wanted a book for clinicals (and class) to help with determining differential diagnosis. This book (referring to 2nd Ed.) gives the practitioner questions to ask the patient (and yourself while thinking about diff. dx.) as well as possible diagnostic tests to order.

Plenty of room for your own notes as well.

I find it useful and worth buying. Small enough for bookbag or clinical bag.

I am a doctor 5 out of 5 stars.
3 of 20 people found this review helpful.

ý am a doctor.I am interesting diagnosis pro. can you help me

Great reference 4 out of 5 stars.
3 of 5 people found this review helpful.

This book, along with Algorithmic Approach to Treatment, are great reference books for the medical student. It is definitely going to be on my office shelf.

Editorial Review:

Lancaster Community Hospital, California. Pocketsized quick-reference manual of diagnostic algorithms designed to lead to the most costeffective progression of diagnostic tests. Trim size 8 1/4 x 4 inches. DNLM: Diagnosis.

Practical WPF Graphics Programming

Jack Xu

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Customer Reviews:
Total reviews: 2 Average rating: 5.0 of 5

Editorial Review:

The Windows Presentation Foundation (WPF) is a next generation graphics platform that is part of .NET 3.0 and .NET 3.5. It allows you to build advanced user interfaces that incorporate documents, media, 2D and 3D graphics, animations, and web-like characteristics. "Practical WPF Graphics Programming" provides all the tools you need to develop professional graphics applications using WPF and C#. This book will be useful for WPF and C# programmers of all skill levels. It provides a complete and comprehensive explanation of the WPF graphics capability, and pays special attention to the details of code implementation. The book shows you how to create a variety of graphics ranging from simple 2D shapes to complex 3D surfaces and interactive 3D models. It includes over 120 code examples, which cover broad array of topics on WPF graphics programming. You will learn how to create a full range of 2D and 3D graphics applications and how to implement custom 3D geometries and shapes that can be reused in your WPF projects. Please visit the author's website for more information about this book at www.authors.unicadpublish.com/~jack_xu.

STL Pocket Reference

Ray Lischner

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Customer Reviews:
Total reviews: 5 Average rating: 4.0 of 5

Cute and handy 4 out of 5 stars.
15 of 22 people found this review helpful.

Cute, if I may use that term for a computer book! Hopefully, you already are familiar with the constructs described in this nifty little guide. Priority queues, queues, sets, iterators ... The book is strictly an aid to memory. You might consider it the hardcopy kin to online manual pages on each term.

Which raises the question. If you have the equivalent information already on your computer, why should you get this book? For some, there is in fact no need. But a surprising (and ironic) number of programmers still prefer hardcopy by their desktops. There is still something appealing about the combined visual, tactile and random access of a good concise reference book which trumps a screen, though we all write to the latter nowadays.

Editorial Review:

The C++ standard library, even the subset known as the STL, is vast. It's next to impossible to work with the STL without some sort of reference at your side to remind you of template parameters, function invocations, return types--indeed, the entire myriad of details you need to know in order to use the STL effectively and get work done. You need a memory-aid. Books that cover the standard library and the STL tend to be quite heavy and large, describing each aspect of the STL in detail. Such books are great when you're not familiar with the library, but get in the way when you simply need to remind yourself of a function name, or the order in which you pass arguments to a function. Programmers familiar with the STL need a small, lightweight memory-aid. That's what the STL Pocket Reference is. It's small, lightweight, and chock-full of information that you can take in at a glance, so you can get on with your work.

Principles of Robot Motion: Theory, Algorithms, and Implementations (Intelligent Robotics and Autonomous Agents)

Howie Choset, Kevin M. Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia E. Kavraki, Sebastian Thrun

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Customer Reviews:
Total reviews: 1 Average rating: 5.0 of 5

Great book on mobile robotics 5 out of 5 stars.
0 of 3 people found this review helpful.

This is a great book on mobile robotics, a lot of methods are explained in the book and its writing is clear and easy to understand. I have used it on several undergraduate and graduate courses that I have taken, I fully recommend it.

Editorial Review:

Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, relating low-level implementation details to high-level algorithmic concepts.