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How to Solve It: Modern Heuristics
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No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using
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Hardcover, 554 pages
Published
September 21st 2004
by Springer
(first published 2004)
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This is a really annoying book.
Yellow Springer books are generally not intended to be read cover-to-cover. This one was approachable enough that I could (and did). I was already familiar with many of the problems and algorithms presented. The bibliography is quite complete, and even if the index is a little skimpy, I've used is as a reference book.
Basically, it's a high-level overview with lots of tantalizing results and pointers to the literature. If you're in CS, it's probably worth picking up ...more
Yellow Springer books are generally not intended to be read cover-to-cover. This one was approachable enough that I could (and did). I was already familiar with many of the problems and algorithms presented. The bibliography is quite complete, and even if the index is a little skimpy, I've used is as a reference book.
Basically, it's a high-level overview with lots of tantalizing results and pointers to the literature. If you're in CS, it's probably worth picking up ...more
A collection of heuristics for solving difficult (NP-complete etc.) problems approximately. You learn most of this material in an artificial intelligence/combinatorial optimization/machine learning college course. One topic I wasn't familiar with is coevolutionary genetic algorithms. If you want to evolve rabbits that excel in escaping from being eaten by foxes, you should also at the same time evolve foxes that excel in catching rabbits. For example, if you want to evolve parallel sorting netwo
...more
Well written and clear, 'How to Solve It' is a condensed collection of approaches to numerical optimization, and explains their applicability to various problems. Both gradient based and gradient free methods are presented. There's really not anything in this book that can't be found on the internet, but I find it fun to flip through occasionally, nonetheless. - a good book for anybody (you needn't be a mathematician) interested in browsing a diverse sampling of optimization techniques.
I've read bits and pieces from this book. It's not as interesting as I originally thought it would be, sadly.
Actually pretty good but couldn't finish it since I had to return it. Definitely will pick it up again in the future.
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