Quantity:

More Buying Choices

27 used & new from �27.00

Have one to sell? Sell yours here

Share your own customer images

Search inside this book

How to Solve It: Modern Heuristics (Hardcover)

by Zbigniew Michalewicz (Author), David B. Fogel (Author) "This is not a book about algorithms ..." (more)

3.5 out of 5 stars See all reviews (2 customer reviews)

RRP:	�39.99
Price:	�35.99 & this item Delivered FREE in the UK with Super Saver Delivery. See details and conditions
You Save:	�4.00 (10%)
	o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o

In stock.
Dispatched from and sold by Amazon.co.uk. Gift-wrap available.

Only 4 left in stock--order soon (more on the way).

Want guaranteed delivery by Friday, November 13? Choose Express delivery at checkout. See Details

17 new from �29.17 10 used from �27.00

Other Editions:	RRP:	Our Price:	Other Offers:
Hardcover (illustrated edition)			8 used & new from �29.95

Special Offers and Product Promotions

This title is part of our Up to 35% Off 1000s of Textbooks offer.

Frequently Bought Together

Customers Who Bought This Item Also Bought

How to Solve it: A New Aspect of Mathematical Method (Penguin Science)

by George Polya

5.0 out of 5 stars (2) �7.18

Combinatorial Optimization: Algorithms and Complexity

by Christos H. Papadimitriou

5.0 out of 5 stars (2) �10.74

Thinking Mathematically

by John Mason

4.8 out of 5 stars (6) �30.98

Ant Colony Optimization

by M Dorigo

�30.35

Pandora's Box: Social and Professional Issues of the Information Age

by Andrew A. Adams

5.0 out of 5 stars (1) �29.39

› Explore similar items

Product details

Hardcover: 554 pages
Publisher: Springer; 2nd ed. Revised and Extended edition (21 Sep 2004)
Language German
ISBN-10: 3540224947
ISBN-13: 978-3540224945
Product Dimensions: 23.6 x 15.7 x 3.8 cm

Average Customer Review: 3.5 out of 5 stars See all reviews (2 customer reviews)

Amazon.co.uk Sales Rank: 289,837 in Books (See Bestsellers in Books)
Popular in this category:
#28 in Books > Science & Nature > Mathematics > Optimisation > Linear Programming

Would you like to update product info or give feedback on images?

See Complete Table of Contents

Customers Viewing This Page May Be Interested in These Sponsored Links

(What is this?)

Download New Solver opens new browser window

Solver.com/Download_Premium_Solver - Upgrade to Excel Premium Solver. Free 15-Day Trial Download.

How to Solve It opens new browser window

www.artofproblemsolving.com - Unique Math Books and Forums Thousands of Users Worldwide

Solve opens new browser window

www.Ask.com - Search for Solve Find Solve

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.

Inside This Book (Learn More)

First Sentence
This is not a book about algorithms. Read the first page

Explore More
Concordance

Suggested Tags from Similar Products

(What's this?)

Be the first one to add a relevant tag (keyword that's strongly related to this product)

heuristics

algorithms

mathematics

computer science

What Do Customers Ultimately Buy After Viewing This Item?

	65% buy the item featured on this page: How to Solve It: Modern Heuristics 3.5 out of 5 stars (2) �35.99
	18% buy How to Solve it: A New Aspect of Mathematical Method (Penguin Science) 5.0 out of 5 stars (2) �7.18
	11% buy Combinatorial Optimization: Algorithms and Complexity 5.0 out of 5 stars (2) �10.74
	4% buy The Algorithm Design Manual 4.5 out of 5 stars (6) �27.48

Explore similar items

Customer Reviews

2 Reviews

5 star:		(0)
4 star:		(1)
3 star:		(1)
2 star:		(0)
1 star:		(0)

Average Customer Review

3.5 out of 5 stars (2 customer reviews)

Share your thoughts with other customers:

Create your own review

Most Helpful Customer Reviews

13 of 14 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

This review is from: How to Solve it: Modern Heuristics (Hardcover)

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.

Comment Comment | Permalink | Was this review helpful to you? Yes No (Report this)

3.0 out of 5 stars Underwhelming, 24 Sep 2009

By	S. Matthews "Sean Matthews" (Mainz, Germany) - See all my reviews

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. As far as I can see, the latter is an isolated hack - empirically it may be effective in some applications, but it is not part of a larger conceptual framework. At least if it is not an isolated hack, then the authors provide no evidence - I note that it gets all of three lines in Russell and Norvig.

More seriously, there isn't even any well-founded discussion of mathematical models of evolution. All that I could find was essentially a citation - not even a discussion - of the 'no free lunch' theorems. No Maynard-Smith, no Kondrashov, not even Hopfield's '78 paper - though other later stuff by Hopfield is cited. And without this - and without a lot of other formal theory stuff as well, if I were being honest - there is no hope of a methodological framework. After all, computers are, unavoidably, formal machines. In the end, the evolutionary models that are discussed are not a lot more than a bunch of gadgets, and the authors are reduced to saying that for your own problems, you are going to have to think up your own gadgets. This, to be blunt, is why evolutionary computing remains a niche research area.

A separate problem is the lack of any perspective w.r.t. other, today more commonly used, technologies that address similar problems. The discussion of neural networks and pattern classification does not mention, e.g., that the benchmark classifier technology today is vector support machines, which substantially outperform neural networks. Or that the standard technology for exploring complex function spaces is Monte-Carlo analysis (strictly, a lot of the techniques that are discussed in the book _are_ actually Monte-carlo methods of one sort or another). This does not mean that the methods discussed are _not_ interesting, just that without a perspective, it is difficult to say whether they are appropriate tools for a job.

In the end it was not clear to me who this book is really aimed at. It is certainly not aimed at me. Senior undergraduates in something like operations research might be a target, but I personally would not use it for a CS or applied math class. And I cannot, honestly, forsee it being a lot of use to me outside the classroom.

*Which anyway, a priori, seems unlikely, since idealised recombinatory evolutionary strategies show rapid information gain (N^1/2) in the size of the genome in suboptimal situations, but near the optimum, parthenogenesis with mutation is a better strategy.

Comment Comment | Permalink | Was this review helpful to you? Yes No (Report this)

Share your thoughts with other customers: Create your own review

› See all 2 customer reviews...

.jsOffDisplayBlock { display: block; } .jsOffDisplayInline { display: inline; } .jsOffVisibility { visibility: visible; } .jsOnDisplayBlock { display: none; } .jsOnDisplayNoneBlock { display: block; } .jsOnDisplayNoneInline { display: inline; } .jsOnDisplayInline { display: none; } .jsOnVisibility { visibility: hidden; } .jqOnDisplayBlock { display: none; } .jqOnDisplayInline { display: none; } .jqOnVisibility { visibility: hidden; } .jqOnDisplayNoneBlock { display: block; } .jqOnDisplayNoneInline { display: inline; }