Good intro to the subject
The course at ODU does not have a textbook, but this would be my recommendation for one. The chapters basically follow the outline for the course, and this book is highly recommended for anyone interested in Computational Intellegence and Design Optimization
Just a brief survey of what CI is. Not really a great book that teaches you the foundations of CI.
In ONE world: 'self-filler'
This book is not really an introduction to Computational Intelligence, for an introductory type of book read David E. Goldberg's Genetic Algorithms Book, it is exceptional in terms of readability, concepts presented and depths it gradually delves into. That is the father of all genetic algorithm/EC/AI books, in my humble opinion.
I know most of the stuff in Engelbrecht's book now, not because I've read it from his book, but because I've learnt it from other sources, do yourself a favor, read more descriptive aind insightful material about AI and CI. the chapters on NN and SOM, GA, EC are very shallow, he just summarizes ideas you can get from reading free sources like, AI webpages, presents a bunch of formulas, and to understand what they mean, you have to refer to the glossary and appendices. Furthermore he is inconsistent in his symbols, the terms represented by his symbols change through-out the book, sometimes even in consecutive chapters.
Stuff he never addresses: Taboo Search, Simulated Annealing aren't even mentioned in the book. Travelling salesman problems, snake in the box, hybrid systems, neuro fuzzy system, adaptive genetic fuzzy systems are all never mentioned. There are no examples of the uses of the material he covers, kind of takes the flavor away from what you're reading. It is bland, fonts are awful, and pictures are drawn with Microcrap's paintbrush. This book will never give you an insight into CI paradigms, if you already know that stuff and want a quick revision, or want a reference for the formulas, get this book, or else it's just a shelf-filler. [...]
A fine overview
The book is divided into four parts. The first part is about neural networks and is covered in much more depth than other parts. The author touches upon every major neural network paradigm and covers fundamental learning techniques. What bothers me about the first part is following:
- it is too theoretical
- the chapter about neural networks performance is too fat, although practical (but it would be better to be more practical about general NN techniques), so i lost interest in it when reading.
The second part about genetic algorithms is interesting and concise, but techniques like cultural evolution and coevolution are flabby explained. Few pages more could be spared.
The third part about swarm intelligence covers two techniques, namely ant colonies and particle optimization. Since there is not much literature about these young techniques, i find them quite well explained.
The last part about fuzzy logic was a bit disappointing, because it seemed like the author was in a rush to finish the book. Too shallow to be of any practical use (but still as a reference).
All in all is the book a good overview with lots of references on more specialized literature. I don't know about any other book to cover such diverse fields in one volume, and therefore recommend it for readers, who do not intend to go into deep study of things.
Check for more reviews on Amazon.com
Similar Products:
|
