Series: Complex Adaptive Systems
Paperback: 232 pages
Publisher: A Bradford Book; Reprint edition (April 29, 1992)
Product Dimensions: 7 x 0.4 x 9 inches
Shipping Weight: 14.9 ounces (View shipping rates and policies)
Average Customer Review: 4.3 out of 5 stars See all reviews (7 customer reviews)
Best Sellers Rank: #988,088 in Books (See Top 100 in Books) #59 in Books > Computers & Technology > Hardware & DIY > Microprocessors & System Design > Control Systems #319 in Books > Textbooks > Computer Science > Artificial Intelligence #660 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics
I am learning by myself the topic of Genetic Algorithms (GA) for my PhD dissertation. Even though this book is written for John H. Holland considered the father of Genetics Algorithms, this is not a basic or easy reading book. The book does not contain any source code and even though it contains some kind of pseudocode, it will not give you a clear idea about how to implement a GA. If you want an introduction book maybe you should look for the Mitchell Melanie's book "An Introduction to Genetic Algorithms" , Fogel's book "Evolutionary Computation vol. 1" or Chamber's book "The Practical Handbook of Genetic Algorithms".The way the author approaches the development of the framework is sometimes overwhelming because the author does not concentrate in one specific case or concept but he mentions all the different possibilities almost at the same time. I think it is worthwhile to buy the book to have it for advanced understanding of the concepts involved in the study of Complex Adaptive System. My approach to learn GA will be reading the above mentioned books and then study this book in a very detailed and slowly way to digest the huge amount of concepts and information provided by it.
This book presents an inspirational synthesis from mathematics, computer science and systems theory addressing genetic algorithms and their role in intelligent engineering/business systems.Topics include: background, a formal framework, illustrations (genetics, economics, game playing, searches, pattern recognition and statistical inference, control and function optimization, and central-nervous system), schemata, the optimal allocation of trials, reproductive plans and genetic operators, the robustness of genetic plans, adaptation of coding and representations, and overview, interim and prospectus.Inclusion of a disk of spreadsheet-based examples would have increased user-friendliness to the sometimes moderately-complex mathematics. Otherwise, this book is a well presented, and useful classic for researchers and software vendors seeking to develop more innovative intelligent products.
Good, however, the .com listing did not say that this text was geared for Ph.D.'s in Mathematics.
There's no source code here. This is the original book from the 70s about the theoretical basis for genetic-style adaptation, and the surprising parallels between evolution, gambling, and learning in general. foundational, clear, mathematical. Probably won't help you pass your assignments though.
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