Introduction

An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.

Major Mechanisms of Biological Evolution

Introduces the major mechanisms of biological evolution.

Examples of Evolution in Nature

Demonstrates many fascinating aspects of evolution in nature with simple, yet illustrative examples.

Branches of Evolutionary Computation

Explains each of the major branches of evolutionary computation: genetic algorithms, genetic programming, evolutionary programming, and evolution strategies.

Programming with Evolutionary Principles

Demonstrates the programming of computers by evolutionary principles using Evolvica, a genetic programming system designed by the author.

Developmental Programs

Shows in detail how to evolve developmental programs modeled by cellular automata and Lindenmayer systems.

Additional Resources

Provides Mathematica notebooks on the Web that include all the programs in the book and supporting animations, movies, and graphics.