Introduction to genetic programming

As a taster of genetic programming, there are some excellent slides from a course at University of Edinburgh, which covers a lot of theory behind genetic algorithms and, more specifically, genetic programming:
"Genetic Algorithms and Genetic Programming 2009-10". 

My favourite book on the subject is "Genetic Programming: An Introduction" by Banzhaf et al. It is a well-written and concise introduction that will give you a sound grounding in all the theory necessary to evolve your own GP populations.

Once you are ready to either tweak existing GP systems or write your own from scratch, "A Field Guide to Genetic Programming" by Poli et al is a priceless resource. It is also available for free as pdf.

After reading through these slides and books (as well as some that turned out to be less helpful), I decided on a tree-based GP approach, where the programs evolved are Lisp-like structures. Below is a simple example from a Tic-tac-toe player. The program's output, in this case, are the MAKEMOVE nodes and each node computes its value based on any subtrees it may have, including the current board state read from SQ_X_Y nodes.

But first some more info on how I got to the stage where I could generate programs like the one above...