David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand.  The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend. 

"An outstanding introduction to a new and important field of computer science." -- Tim Watson, The Computer Journal "This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing." -- Chris Robbins, Computing Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics. 

Some of the most startling achievements in the use of computers to automate design are being accomplished by the use of evolutionary search algorithms to evolve designs. 

Evolutionary Design By Computers provides a showcase of the best and most original work of the leading international experts in Evolutionary Computation, Engineering Design, Computer Art, and Artificial Life. By bringing together the highest achievers in these fields for the first time, including a foreword by Richard Dawkins, this book provides the definitive coverage of significant developments in Evolutionary Design. 

This book explores the related sub-areas of Evolutionary Design, such as: 

-- design optimization
-- creative design
-- the creation of art
-- artificial life

It shows for the first time how techniques in each area overlap, and promotes the cross-fertilization of ideas and methods. A CD-ROM is included with the book, providing the reader with hands-on activities, evolved images, animations, and source-code related to the text. Evolutionary Design By Computers will be an essential resource for researchers in academia and industry. 

"Read this book, and even if you don't read it, buy it and display it proudly. Scientists, engineers, and coffee tables the world over should be interested in the revised edition of the seminal book that first gathered and developed the critical mass of ideas from mathematics, computational science, and systems theory necessary to launch and fuel the ongoing revolution in complex innovating systems. From mathematical optimization to the immune system, from machine learning to the central nervous system, from automatic control systems to even something as complex as human society itself, all innovating systems fall under the spell of Holland's mathematical-computational magic, and all individuals interested in understanding or engineering such systems ignore Holland at their peril." --David E. Goldberg, University of Illinois at Urbana-Champaign 

Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that intitiated this field of study, presenting the theoretical foundations and exploring applications. 

Zbigniew Michalewicz's Genetic Algorithms + Data Structures = Evolution Programs has three sections. The first section is a straightforward introduction to genetic algorithms. In the second section, Michalewicz describes how to apply genetic algorithms to numerical optimization. Michalewicz, who is a pioneer in this field, discusses the rationale for using genetic algorithms for numerical optimization and describes several experiments that show how this new type of genetic algorithm performs. The author devotes the third section of the book to evolution programs. 

An introduction to genetic algorithms and their application in science and engineering. Genetic algorithms are a new and rapidly expanding field and many new applications are being discovered in a variety of disciplines. Some of their uses include solving difficult design problems in science and engineering, optimizing scheduling in operations research and optimizing decision making and portfolio design in management.