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How to Solve It: Modern Heuristics

How to Solve It: Modern Heuristics [Kindle Edition]

Zbigniew Michalewicz , David B. Fogel
4.0 out of 5 stars  See all reviews (3 customer reviews)

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Product Description


"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)

Product Description

"This book is the only source that provides comprehensive, current, and correct information on problem solving using modern heuristics. It covers classic methods of optimization, including dynamic programming, the simplex method, and gradient techniques, as well as recent innovations such as simulated annealing, tabu search, and evolutionary computation. Integrated into the discourse is a series of problems and puzzles to challenge the reader. The book is written in a lively, engaging style and is intended for students and practitioners alike. Anyone who reads and understands the material in the book will be armed with the most powerful problem solving tools currently known.

This second edition contains two new chapters, one on coevolutionary systems and one on multicriterial decision-making. Also some new puzzles are added and various subchapters are revised."

Product details

  • Format: Kindle Edition
  • File Size: 7416 KB
  • Print Length: 554 pages
  • Publisher: Springer; Enlarged 2nd edition (8 Dec 2004)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • Text-to-Speech: Enabled
  • X-Ray:
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Bestsellers Rank: #499,235 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Customer Reviews

4.0 out of 5 stars
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Most Helpful Customer Reviews
14 of 15 people found the following review helpful
4.0 out of 5 stars Wide, deep and good fun. Well worth reading. 19 Mar 2001
By A Customer
Although a text book, this book is fun. It is both a good review of evolutionary algorithms and an excellent introduction to problem solving.
Algorithms are not in general given in full and there is no source code, so you will still have to put some effort in if you have a real problem to solve. However this is one of the points of the book: real problems are complex, you can't just use a recipe.
Coverage of other search methods such as neural nets is not so extensive, but there is reasonable coverage of more traditional methods.
On top of all this, there are numerous problem solving problems. These are all good fun and need nothing more than paper and pen.
A good book for the train and for the library. And there arn't many of those.
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15 of 16 people found the following review helpful
3.0 out of 5 stars Underwhelming 24 Sep 2009
Having read a couple of very positive reviews, I was looking forward to this, but now that it is here, and I've had a chance to read it, I'm very disappointed. It strikes me as pretty shallow, and it definitely takes the name of Georg Polya in vain. (Michaelewicz also writes 'business-oriented' books on decision support - you can tell). It certainly has little, conceptually, to do with Polya's 'How to solve it' (in fact, given the complete lack of any formal theoretical development, the authors are lucky that the man is safely dead). A more accurate title would be 'a bunch of stuff on optimisation, mostly about genetic algorithms and traveling salesman problems, but with a bit on neural nets and fuzzy logic thrown in'. These three technologies used to get sexy articles in the popular computer press about 10 to 20 years ago. It is interesting why the three always seem to crop up together, but they do - or at least they did.

Anyway, the core agenda, which is not heuristics, does poke out at various points. On page 190 there is a revealing passage bout the elusive 'Holy Grail' of 'a perfect evolutionary algorithm for the TSP [Travelling Salesman Problem]'. Now, the world in general would be fascinated by a polynomial solution to the TSP, but the world in general - sorry to say - doesn't actually give a toss if that solution is evolutionary.*

As I said, I was unhappy about the complete lack of real theoretical background which would put any of the discussed methods in perspective/context. The discussion of simulated annealing, for instance, is absent any of the underlying (and powerful) intuitions from statistical physics which, if nothing else, makes the technique much richer, and not conceptually comparable to, tabu search, with which it is discussed in parallel.
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0 of 5 people found the following review helpful
5.0 out of 5 stars Easy transaction and great book! 19 Feb 2012
By Abbie
This book arrived quite soon after I had paid for it. Very please with the transaction and the book is great too!
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Most Helpful Customer Reviews on (beta) 4.6 out of 5 stars  23 reviews
73 of 75 people found the following review helpful
5.0 out of 5 stars Outstanding and Unique Contribution 25 Jan 2001
By William T. Scherer - Published on
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.
38 of 38 people found the following review helpful
5.0 out of 5 stars extremely well written 9 Sep 2004
By Digital Puer - Published on
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. Interestingly, in the summary chapter, there is an anecdote on the 1980s TV show Magnum PI (I even remember the mentioned scene myself), further revealing the friendly, plain-English tone of the book. Perhaps the best part of the book is that the numerical mathematical discourse is kept at a minimum (used largely for the NLP problems), so people who haven't taken calculus in ages (like me) can easily enjoy the book.

As an added bonus(!), between each chapter is a brain-teaser problem like those found in those legendary Microsoft interview questions.

My only complaint is that there is no simple analysis of the running time complexity of each algorithm, which even in its simplest form would have been a great thing to read about.

In summary, this book is an excellent read if you enjoy the topics covered. Highly recommended.
27 of 28 people found the following review helpful
5.0 out of 5 stars A comprehensive overview of problem solving techniques 25 Jan 2000
By David Czarnecki - Published on
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.
43 of 48 people found the following review helpful
5.0 out of 5 stars Zen and the art of problem solving. 1 Mar 2000
By Mariusz Milik - Published on
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).
33 of 36 people found the following review helpful
5.0 out of 5 stars things that make you go hmm... 16 Mar 2000
By Gene Cheung - Published on
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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