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.

chapter 1|3 pages

Introduction to evolutionary computation

chapter 4|4 pages

Principles of evolutionary processes

chapter 5|13 pages

Principles of genetics

chapter 6|19 pages

A history of evolutionary computation

chapter 8|17 pages

Genetic algorithms

chapter 9|8 pages

Evolution strategies

chapter 10|14 pages

Evolutionary programming

chapter 12|10 pages

Learning classifier systems

chapter 13|3 pages

Hybrid methods

chapter 14|5 pages

Introduction to representations

chapter 15|4 pages

Binary strings

chapter 16|3 pages

Real-valued vectors

chapter 17|12 pages


chapter 18|4 pages

Finite-state representations

chapter 19|5 pages

Parse trees

chapter 21|3 pages

Other representations

chapter 22|6 pages

Introduction to selection

chapter 24|6 pages

Tournament selection

chapter 25|8 pages

Rank-based selection

chapter 26|6 pages

Boltzmann selection

chapter 27|4 pages

Other selection methods

chapter 28|7 pages

Generation gap methods

chapter 30|7 pages

Interactive evolution

chapter 32|19 pages

Mutation operators

chapter 33|52 pages


chapter 34|23 pages

Other operators