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" (?). 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."

Genetic programming (GP) is an evolutionary computation (EC)1 technique 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 statement 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, summarised 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.

Figure 1.1: The basic control flow for genetic programming, where survival of the fittest is used to find solutions.

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