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How to Solve It: Modern Heuristics

4.6 out of 5 stars 24 customer reviews
ISBN-13: 978-3540660613
ISBN-10: 3540660615
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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)

About the Author

Michalewicz, University of North Carolina at Charlotte

David B. Fogel is chief executive officer of Natural Selection, Inc. in La Jolla, CA--a small business focused on solving difficult problems in industry, medicine, and defense using evolutionary computation, neural networks, fuzzy systems, and other methods of computational intelligence. Dr. Fogel's experience in evolutionary computation spans 20 years and includes applications in pharmaceutical design, computer-assisted mammography, data mining, factory scheduling, financial forecasting, traffic flow optimization, agent-based adaptive combat systems, and many other areas. Prior to cofounding Natural Selection, Inc. in 1993, Dr. Fogel was a systems analyst at Titan Systems, Inc. (1984-1988), and a senior principal engineer at ORINCON Corporation (1988-1993).
Dr. Fogel received his Ph.D. degree in engineering sciences (systems science) from the University of California at San Diego (UCSD) in 1992. He earned an M.S. degree in engineering sciences (systems science) from UCSD in 1990, and a B.S. in mathematical sciences (probability and statistics) from the University of California at Santa Barbara in 1985. He has taught university courses at the graduate and undergraduate level in stochastic processes, probability and statistics, and evolutionary computation. Dr. Fogel is a prolific author in evolutionary computation, having published over 50 journal papers, as well as 100 conference publications, 20 contributions in book chapters, two videos, four computer games, and six books--most recently, "Blondie24: Playing at the Edge of AI" (Morgan Kaufmann, 2002). In addition, Dr. Fogel is coeditor in chief of the "Handbook of Evolutionary Computation" (Oxford, 1997) and was the foundingeditor-in-chief of the "IEEE Transactions on Evolutionary Computation" (1996-2002). He serves as editor-in-chief for the journal "BioSystems" and is a member of the editorial board of several other international technical journals.
Dr. Fogel served as a Visiting Fellow of the Australian Defence Force Academy in November 1997, and is a member of many professional societies including the American Association for the Advancement of Science, the American Association for Artificial Intelligence, Sigma Xi, and the New York Academy of Sciences. He was the founding president of the Evolutionary Programming Society in 1991 and is a Fellow of the IEEE, as well as an associate member of the Center for the Study of Evolution and the Origin of Life (CSEOL) at the University of California at Los Angeles. Dr. Fogel is a frequently invited lecturer at international conferences and a guest for television and radio broadcasts. His honors and awards include the 2001 Sigma Xi Southwest Region Young Investigator Award, the 2003 Sigma Xi San Diego Section Distinguished Scientist Award, the 2003 SPIE Computational Intelligence Pioneer Award, and the 2004 IEEE Kiyo Tomiyasu Technical Field Award. --This text refers to an alternate Hardcover edition.
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Product Details

  • Hardcover: 467 pages
  • Publisher: Springer (March 1, 2004)
  • Language: English
  • ISBN-10: 3540660615
  • ISBN-13: 978-3540660613
  • Product Dimensions: 9.5 x 6.5 x 1.2 inches
  • Shipping Weight: 1.6 pounds
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (24 customer reviews)
  • Amazon Best Sellers Rank: #1,287,224 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

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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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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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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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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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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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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