The field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field.
Table of Contents
WHY EVOLUTIONARY COMPUTATION?
Introduction to evolutionary computation
Possible applications of evolutionary computation
Advantages (and disadvantages) of evolutionary computation over other approaches
EVOLUTIONARY COMPUTATION: THE BACKGROUND
Principles of evolutionary processes
Principles of genetics
A history of evolutionary computation
EVOLUTIONARY ALGORITHMS AND THEIR STANDARD INSTANCES
Introduction to evolutionary algorithms
Derivative methods in genetic programming
Learning classifier systems
Introduction to representations
Guidelines for a suitable encoding
Introduction to selection
Proportionary selection and sampling algorithms
Other selection methods
Generation gap methods
A comparison of selection mechanisms
Introduction to search operators
"This new volume contains some extended material and provides basic information on evolutionary algorithms … This book provides an interesting reference for theorists, teachers, and practitioners also."
-H.D. Hecker, Zentralblatt Math