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How to Solve It: Modern Heuristics Hardcover – December 8, 2004

ISBN-13: 978-3540224945 ISBN-10: 3540224947 Edition: Enlarged 2nd

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How to Solve It: Modern Heuristics + How to Solve It: A New Aspect of Mathematical Method (Princeton Science Library) + How to Prove It: A Structured Approach, 2nd Edition
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Product Details

  • Hardcover: 554 pages
  • Publisher: Springer; Enlarged 2nd edition (December 8, 2004)
  • Language: English
  • ISBN-10: 3540224947
  • ISBN-13: 978-3540224945
  • Product Dimensions: 9.2 x 1.2 x 6.1 inches
  • Shipping Weight: 2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (23 customer reviews)
  • Amazon Best Sellers Rank: #190,434 in Books (See Top 100 in Books)

Editorial Reviews

Review

The March 2002 issue of ACMs Computing Reviews identifies a review of "How to Solve It" as the best review they published in 2001. The review is then reprinted in its entirety. Reviewer: H. van Dyke Parunak.
Excerpt: Like its predecessor, the new How to Solve It, combines deep mathematical insight with skilled pedagogy. Puzzle lovers will seek out the book for its insightful discussion of many intriguing brain twisters. Students of computational methods will find it an accessible but rigorous introduction to evolutionary algorithms. Teachers will learn from its expositions how to make their own subject matter clearer to their students. Polya would be honored to know that his spirit lives on in the computer age.

From the reviews of the second edition:

"This is an outstanding book. It takes the reader close to the current knowledge frontier … . The book’s writing style is lively and educational, and this makes it extremely interesting … . is intended for students and practitioners. … is an excellent choice for a course on heuristics … . One of the most comprehensive views … is provided in this book. It is written to be read and understood … . is a must-read and must-have for anyone engaged in the art of problem solving." (Dimitrios Katsaros, Computing Reviews, April, 2005)

--This text refers to an out of print or unavailable edition of this title.

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Customer Reviews

I found the book to be extremely well written and very compelling to read.
Digital Puer
It says, "if you have a hammer, everything looks like a nail." Read and find out why the more textbooks you read, the more a screw looks like a nail!
Gene Cheung
I use it as the main text book for my Computer Heuristic course in Colombia.
tolarteh

Most Helpful Customer Reviews

73 of 75 people found the following review helpful By William T. Scherer on January 25, 2001
Format: Hardcover
This book provides a very accessible and contemporary treatment of optimization. Of particular interest is the problem solving orientation of the book as opposed to a tool-based approach to optimization and heuristics. The writing style of the book makes the book very interesting and readable - a rare thing to say about technical books! I used this book in a Master's class on Heuristics (Systems Engineering, University of Virginia) and received the most positive textbook reviews I have seen in my fifteen years of teaching. The book is an excellent choice for a course on heuristics, mathematical modeling, optimization, etc., and could be used in an advanced undergraduate class or a graduate class. In addition, the book is ideal for practitioners who may not have had exposure to modern heuristics in their education or practice, or those who want to get updated on the latest developments in the field.
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38 of 38 people found the following review helpful By Digital Puer on September 9, 2004
Format: Hardcover
I read this book while taking an advanced class in heuristics. I found the book to be extremely well written and very compelling to read. Although dealing with advanced topics, the authors' friendly and clear writing style makes it accessible to anyone with a CS background.

The first half of the book is on search heuristics, covering methods such as traditional searches (exhaustive search, greedy algorithms, divide and conquer, dynamic programming, A*, etc), methods to escape local optima (simulated annealing, tabu search), and, perhaps most interesting of all, evolutionary algorithms. I later found out that these topics are typically taught in undergraduate artificial intelligence courses, an elective I never took. The second half of the book covers even more advanced areas, such as contraint-handling, neural networks, and fuzzy systems.

The authors use three recurring example applications to demonstrate each search technique: the boolean satisfiability problem (SAT), travelling salesman (TSP), and a nonlinear programming problem (NLP). I really liked the consistent use of these three examples, as they give a sense of continuity throughout the book that helps the reader compare search techniques clearly. I had of course studied the TSP problem in my undergraduate algorithms class but never in the context of such interesting approximation algorithms. In my heuristics class we had assignments to implement the TSP search problem using the Lin-Kernighan method, dynamic programming, and an evolutionary algorithm.

The written English in this book is simply outstanding and crystal-clear, which was something of a shock since I was unable to even pronounce the first author's name. The writing is in a very friendly tone with elements of humour dispersed throughout.
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27 of 28 people found the following review helpful By David Czarnecki on January 25, 2000
Format: Hardcover
This book provides one of the most comprehensive views of modern techniques in problem solving. The authors use a number of classic problems to illustrate conventional heuristics as well as giving you a solid and working knowledge of more modern evolutionary techniques. The appendicies provide a good introduction to background information on probability theory and statistics used throughout the book, as well as projects for further exploration. Scattered throughout the text are complete and up-to-date references that can be used by the reader to delve deeper into certain topic areas. This book is written to be read and understood by both students and experienced researchers in the field.
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43 of 48 people found the following review helpful By Mariusz Milik on February 29, 2000
Format: Hardcover
Don't think that this book is just another version of numerical recipes or "how to" for optimization methods. For me it is about something absolutely different. About breaking old, bad habits in problem solving and looking for the simplest and the most elegant solutions for the given problem. Sometimes it will be something complicated, like competitive neural network, but sometimes the solution will be just: "let's assume that there's no river" (see page 185 of this book). Don't put artificial intelligence where just the common sense will be absolutely enough. I remember some of the problems presented there from my high school years. I had more problems with solving them today than it was many years ago. It looks that we are loosing somewhere, in the process of education, the possibility to simplify problems and rather try to solve them by "brute force". This book may give you this fresh look again (I hope).
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33 of 36 people found the following review helpful By Gene Cheung on March 16, 2000
Format: Hardcover
READ: this is not just another optimization book! Instead of spoon-feeding one technique after another (do a search on "optimization" and you will know what i mean), it challenges you to think CREATIVELY. It says, "if you have a hammer, everything looks like a nail." Read and find out why the more textbooks you read, the more a screw looks like a nail! (and remedy to return to reality)
Despite working on algorithms for years in graduate school, for the first time there is a book that looks at problem solving with a fresh, unbiased perspective. Definitely my best buy in years.
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28 of 31 people found the following review helpful By Shlomo Yona on October 31, 2002
Format: Hardcover
I first ordered this book thinking it was George Polya 's book "How to solve it", then I realized it wasn't and I bought it anyway since I thought it might turn out as a "must read" book, just like Polys'a book.
One one hand it was a dissapointment, because the books are not written in the same manner and don't attact similar problelsm.
But then, this book makes you look into problems, and realize that usually we people are usually good in solving problems of the sort we learned how to (well... duh!), but surprisingly, we have a hard time solving even trivial problems if they are not placed in the context we got used to seeing them.
This book comes and tries to make things better in this department, showing you some general methods for solving problems, and also showing problems and suggested solutions along with a long discussion.
You should be able, once you've read the book and put your mind to it, to be better in understanding problems, understanding which tool to use for solving them and finally, understanding the tools enough to be able to actually solve the problem.
I enjoyed the overview of methods, and there are many such methods throughout the book (perhaps a complementary book for learning which "machine learning" methods are available these days and what sorts of problems they are useful for solving would be Tom Mitchell's "Machine Learning" book).
I wasn't sorry for buying this book. I'm happy I was fortunate enough to bump into it.
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