Algorithms, by Richard Johnsonbaugh and Marcus Schaefer, is intended for an upper-level undergraduate or graduate course in algorithms. The authors. For upper-level undergraduate and graduate courses in algorithms. Filling the void left by other algorithms books, Algorithms and Data Structures provides an. Authors: Richard Johnsonbaugh · Marcus Schaefer Filling the void left by other algorithms books, Algorithms and Data Structures provides an approach that.
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Filling the void left by other algorithms books, Algorithms and Data Structures provides an approach that emphasizes design techniques.
The volume includes application of algorithms, examples, end-of-section exercises, end-of-chapter exercises, hints and solutions to selected exercises, figures and notes to help the reader master the design and analysis of algorithms. This volume covers data structures, searching techniques, divided-and-conquer sorting and selection, greedy algorithms, dynamic programming, text searching, computational algebra, P and NP and parallel algorithms. For those interested in a better understanding of algorithms.
Algorithms is written for an introductory upper-level undergraduate or graduate course in algorithms. He has degrees in computer science and mathematics from the University of Oregon, Yale University, and the University of Illinois at Chicago. He holds degrees in computer science and mathematics from the University of Chicago and the Universitat Karlsruhe. He has authored and co-authored several articles on complexity theory, computability, and graph theory. Would you like to tell us about a lower price?
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Customers who viewed this item also viewed. Algorithms and Data Structures: The Algorithm Design Manual. From the Back Cover Algorithms is written for an introductory upper-level undergraduate or graduate course in algorithms.
Key Features Links theory to real-world applications such as data compression, region-finding in digital pictures, cellular phone networks, and the implementation of agrep. Includes five chapters that emphasize design techniques: Features a collection of techniques, including approximation, parameterization a recent area of researchand use of heuristics, to deal with NP-complete problems.
Contains more than carefully developed and classroom-tested exercises, from routine to challenging. About one-third of the end-of-section exercises include solutions. Includes more than worked examples, which provide motivation, clarify concepts, and show how to develop algorithms, demonstrate applications of the theory, and elucidate proofs. Pearson August 10, Language: I’d like to read this book on Kindle Don’t have a Kindle?
Share your thoughts with other customers. Write a customer review. Read reviews that mention genetic algorithms computer science machine learning good introduction graduate students introduction to genetic introduction to algorithms chapter text examples code algorithm gas presented programming textbook mathematical theory chapters optimization.
Showing of 29 reviews. Top Reviews Most recent Top Reviews. There was a problem filtering reviews right now. Please try again later. I was looking for an automated approach to finding an optimum run sequence through a changeover matrix. The programming examples gave me the elements I needed to experiment and then fine tune the approach for a working search algorithm.
I found the book a good companion in my “voyage of discovery”. For me, the book works two levels, the basic pieces to “play with” are presented clearly in chapters 1 and 3, and practical implementation suggestions are spread throughout the text.
By developing programs in Visual Basic, experimenting with search parameters and re-reading sections of this book – I learned something new! I took an AI class and bought this.
The professor is very old-school and still uses overhead projectors and hands out paper notes instead of something like PDF. The book is definitely dated here inbut the ideas presented therein are valid. I would look elsewhere for a modern genetic algorithms book, though. Unless your professor is old-school and has textbooks older than you are.
The code examples are largely irrelevant: So if you want to play along and run the code you either need to locate an old and CRT monitor, or translate the code into something that actually runs in this century.
Good book, explains many algorithms in more plain, easier to understand English than the Introduction to Algorithms by Cormen, Lieserson, Rivest, and Stein. Although it is different in the amount and focus of material. Both books used in conjunction is helpful. This book absolutely delivers more than I ever wanted to know about genetic algorithms. Worth it just for the first few chapters.
I agree with another reviewer who said the book was unnecessarily long. Genetic Algorithms are a great programming tool, and there are some tips and tricks that can help your programs converge faster and more accurately, but this book had a lot of redundant information.
If you are interested in using GA for solution-finding, I doubt you’ll find much useful in this book beyond the first chapter or so. Many of the examples later in the book were so specific that I couldn’t see how they could be usefully generalized. Really optimizing a GA approach for a specific problem domain takes a fair amount of tuning, and this book won’t help much with that. I think time spent surfing siteseer or other publication sites would be better spent than reading this book.
We are using this book as a text in our Computer Science II course. Unfortunately, this is a somewhat difficult book to try and learn from.
The descriptions are very dense, filled with many proofs, and little in the way of an explanation as to what an algorithm does, or what it’s purpose is.
In other words, rather than simply stating what an algorithm is used for and the general ideas behind it in a couple clear paragraphs, it spreads this information out between unclear and hard to follow proofs that skip over important stepsusing difficult language, and between multiple “failed attempts” which try to show how to arrive at the optimal way of implementing an algorithm without clearly stating so. I want to state now that I spend a lot of time reading dense texts from other computer science books, to Microsoft documentation, to even some Wikipedia articles.
While I can bear through this book, it is one of the more difficult things I’ve had to deal with. In my opinion, its worse than the level of perceived greatness from teachers, and practical uselessness to students as is found in many math books that can prove everything and explain almost nothing.
If you’re a professor, before choosing this book, or any other really, try to see if it at least has clear explanations in addition to whatever else one might want. Because, as a student who often relies on the textbook for learning things, this one is near impossible.
I’ satified; I’ knew this book in your sit It arrived at many time ago Itis a interesting and serious book. See all 29 reviews. What other items do customers buy after viewing this item? Data Structures and Algorithms Made Easy: Algorithms 4th Edition Hardcover. Cracking the Coding Interview: Pages with related products. See and discover other items: There’s a problem loading this menu right now.
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