Page 3 - 49A Field Guide to Genetic Programming

Preface

Genetic programming (GP) is a collection of evolutionary computation tech-
niques that allow computers to solve problems automatically. Since its in-
ception twenty years ago, GP has been used to solve a wide range of prac-
tical problems, producing a number of human-competitive results and even
patentable new inventions. Like many other areas of computer science, GP
is evolving rapidly, with new ideas, techniques and applications being con-
stantly proposed. While this shows how wonderfully prolific GP is, it also
makes it difficult for newcomers to become acquainted with the main ideas
in the field, and form a mental map of its different branches. Even for people
who have been interested in GP for a while, it is difficult to keep up with
the pace of new developments.
Many books have been written which describe aspects of GP. Some
provide general introductions to the field as a whole. However, no new
introductory book on GP has been produced in the last decade, and anyone
wanting to learn about GP is forced to map the terrain painfully on their
own. This book attempts to fill that gap, by providing a modern field guide
to GP for both newcomers and old-timers.
It would have been straightforward to find a traditional publisher for such
a book. However, we want our book to be as accessible as possible to every-
one interested in learning about GP. Therefore, we have chosen to make it
freely available on-line, while also allowing printed copies to be ordered in-
expensively from http://lulu.com. Visit http://www.gp-field-guide.
org.uk for the details.
The book has undergone numerous iterations and revisions. It began as
a book-chapter overview of GP (more on this below), which quickly grew
to almost 100 pages. A technical report version of it was circulated on the
GP mailing list. People responded very positively, and some encouraged us
to continue and expand that survey into a book. We took their advice and
this field guide is the result.

Acknowledgements

We would like to thank the University of Essex and the University of Min-
nesota, Morris, for their support.