This paper introduces genetic programming (GP) a set of evolutionary computation techniques for getting computers to automatically solve problems without having to tell them explicitly how to do it. Since its inception, GP has been used to solve many practical problems, producing a number of human competitive results and even patentable new inventions. We start with a gentle introduction to the basic representation, initialisation and operators used in GP, complemented by a step by step description of their use for the solution of an illustrative problem. We then progress to discuss a variety of alternative representations for programs and more advance specialisations of GP. A multiplicity of real-world applications of GP are then presented to illustrate the scope of the technique. For the benefits of more advanced readers, this is followed by a series of recommendations and suggestions to obtain the most from a GP system. Although the paper has been written with beginners and practitioners in mind, for completeness we also provide an overview of the theoretical results and models available to date for GP. The paper is concluded by an appendix which provides a plethora of pointers to resources and further reading.