Page 15 - 49A Field Guide to Genetic Programming

Chapter 1

Introduction

The goal of having computers automatically solve problems is central to
artificial intelligence, machine learning, and the broad area encompassed by
what Turing called “machine intelligence” (Turing, 1948). Machine learning
pioneer Arthur Samuel, in his 1983 talk entitled “AI: Where It Has Been
and Where It Is Going” (Samuel, 1983), stated that the main goal of the
fields of machine learning and artificial intelligence is:

“to get machines to exhibit behaviour, which if done by humans,
would be assumed to involve the use of intelligence.”
1
Genetic programming (GP) is an evolutionary computation (EC) tech-
nique that automatically solves problems without requiring the user to know
or specify the form or structure of the solution in advance. At the most
abstract level GP is a systematic, domain-independent method for getting
computers to solve problems automatically starting from a high-level state-
ment of what needs to be done.
Since its inception, GP has attracted the interest of myriads of people
around the globe. This book gives an overview of the basics of GP, sum-
marised important work that gave direction and impetus to the field and
discusses some interesting new directions and applications. Things continue
to change rapidly in genetic programming as investigators and practitioners
discover new methods and applications. This makes it impossible to cover
all aspects of GP, and this book should be seen as a snapshot of a particular
moment in the history of the field.

1 These are also known as evolutionary algorithms or EAs.

1