Rating: 
Summary: Good introduction to GAs
Review: A very good introduction to GAs. It shows the theoretical and mathematical foundations of GAs and explains the mechanics in a very simple and affordable way. Easy to read and understand even for newbies.
Rating:
Summary: Very good for begginer and intermediate level
Review: At the beginning of the book you will find a nice intro of GA, some comparison with other techniques, a mathematical foundation and some fields of application. Then you get into the real stuff. Coding(in Pascal) of the basic structures of GA and some more sophisticated, like: scaling fitness, inversion, diploid cells etc... there's also a step-by-step simulation to make you comprehend how the GA really works.
Rating:
Summary: Very good for begginer and intermediate level
Review: At the beginning of the book you will find a nice intro of GA, some comparison with other techniques, a mathematical foundation and some fields of application. Then you get into the real stuff. Coding(in Pascal) of the basic structures of GA and some more sophisticated, like: scaling fitness, inversion, diploid cells etc... there's also a step-by-step simulation to make you comprehend how the GA really works.
Rating:
Summary: a classic
Review: Golberg's book is a classic in the field of genetic algorithms. It introduces the reader to the field of genetic algorithms in a clear and understandable manner, while at the same time maintaining a sense of scientific analysis and promise. If you are interested in the field, you should own this book.
Rating: 
Summary: Read a review article instead!
Review: I agree with another reviewer who said the book was unnecessarily long. Genetic Algorithms are a great programming tool, and there are some tips and tricks that can help your programs converge faster and more accurately, but this book had a lot of redundant information.
If you are interested in using GA for solution-finding, I doubt you'll find much useful in this book beyond the first chapter or so. Many of the examples later in the book were so specific that I couldn't see how they could be usefully generalized. Really optimizing a GA approach for a specific problem domain takes a fair amount of tuning, and this book won't help much with that.
I think time spent surfing siteseer or other publication sites would be better spent than reading this book.
Rating:
Summary: Explains *and* entertains
Review: I bought this book while I was a working professional. It is one of the few textbooks that I have ever read straight through, like a novel. In addition to making everything clear and interesting, the book was even funny at times! I didn't think that was allowed in textbooks. ;-)
Rating:
Summary: Provided me with the elements of a solution
Review: I was looking for an automated approach to finding an optimum run sequence through a changeover matrix. The programming examples gave me the elements I needed to experiment and then fine tune the approach for a working search algorithm. I found the book a good companion in my "voyage of discovery".For me, the book works two levels, the basic pieces to "play with" are presented clearly in chapters 1 and 3, and practical implementation suggestions are spread throughout the text. By developing programs in Visual Basic, experimenting with search parameters and re-reading sections of this book - I learned something new!
Rating:
Summary: The definitive introduction to genetic algorithms
Review: More than seven years after publication, David Goldberg's clear prose, straightforward code examples, and solid theoretical coverage keeps "the blue book" head-and-shoulders above any other text on this most intriguing of algorithmic directions. This is the book that lifted genetic algorithms from obscurity to one of the most discussed (and misunderstood) of emerging technologies.Goldberg did not invent genetic algorithms (that honor goes to either Nature or John Holland, depending on your personal belief system), but he did make sure that they could be understood by any interested programmer. The source code is in Pascal, which may not be to everyone's taste, but is certainly readable by anyone with a programming background. - Larry O'Brien (Editor, AI Expert Magazine 1990-1994
Rating:
Summary: The definitive introduction to genetic algorithms
Review: More than seven years after publication, David Goldberg's clear prose, straightforward code examples, and solid theoretical coverage keeps "the blue book" head-and-shoulders above any other text on this most intriguing of algorithmic directions. This is the book that lifted genetic algorithms from obscurity to one of the most discussed (and misunderstood) of emerging technologies.
Goldberg did not invent genetic algorithms (that honor goes to either Nature or John Holland, depending on your personal belief system), but he did make sure that they could be understood by any interested programmer. The source code is in Pascal, which may not be to everyone's taste, but is certainly readable by anyone with a programming background. - Larry O'Brien (Editor, AI Expert Magazine 1990-1994
Rating: 
Summary: Needs updating
Review: OK, I agree with the previous reviewers: it's the classical textbook for GAs. But it definitely needs updating, as it's a 15-year old book and much has been done in the area. Niching methods, for example, are just outlined. I'd recommend Melanie Mitchell's book instead of this one.
|
