Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Third Edition (IEEE Press Series on Computational Intelligence)

By David B. Fogel

Book Overview

In this revised and significantly expanded second edition, distinguished scientist Dr. David B. Fogel presents the latest advances in both the theory and practice of evolutionary computation to help you keep pace with the most recent developments in this fast-changing field. In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research today. Fogel thoroughly reviews the "no free lunch" theorem and includes a discussion of findings that challenge the very foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence. Evolutionary Computation features an unparalleled integration of history with state-of-the-art theory and practice for engineers, professors, and graduate students of evolutionary computation and computer science who need to keep up to date in this developing field.

• Format: Hardcover
• Language: English
• ISBN: 078035379X
• ISBN13: 9780780353794
• Release Date: August 1996
• Publisher: Wiley-IEEE Press
• Length: 290 Pages
• Weight: 1.40 lbs.
• Dimensions: 0.9" x 6.3" x 9.4"

Customer Reviews

for researchers and students

Ignore the gushing blurb on the back cover about the book having the latest tools and techniques to let computers learn. While the methods are indeed state of the art when they were written, true learning by computers is still elusive. But so long as you keep that reality in mind, the text can indeed be useful. We see the span of ideas in evolutionary computing. Aided in no small part by the massive and continued increase in computational power. Fogel laments that the book's ideas are still typically outside what is generally taken to be Artificial Intelligence. The book strives to be both a text for researchers and for students. Though of course the two groups overlap. For researchers, each chapter has a long list of references to journal papers and monographs, so that you can go directly to many of the original sources. While for students, the chapters come with a non-trivial set of exercises, that usually involve some programming.

The book provides a solid foundation on the subject.

This is an introductory text useful for teaching at graduate levels in Computer/ Information Sciences. The first two chapters provide an overview of the subject and its relationship with other relevant areas. Chapter 4 covers the analysis of the GA with special emphasis to convergence of the algorithm.This is the main chapter of the book. The presentation style of the book is very beautiful.The book should be read by everyone interested in the disciplines of genetic algorithms and/or soft computing.