The field of GP took off in the early 1990's, driven in significant part by the publication of (Koza, 1992). Those early days were characterised by the exponential growth common in the initial stages of successful technologies. Many influential papers from that period can be found in the proceedings of the International Conference on Genetic Algorithms (ICGA-93, ICGA-95), the IEEE conferences on Evolutionary Computation (EC-1994), and the Evolutionary Programming conferences. A surprisingly large number of these are now available on-line, and we've included as many URLs as we could in the bibliography.1 After almost twenty years, GP has matured and is used in a wondrous array of applications from banking to betting, from bomb detection to architectural design, from the steel industry to the environment, from space to biology, and many others (as we have seen in Section 12 ).
In 1996 it was possible to list almost all the studies and applications of GP (Langdon, 1996), but today the range is far too great. In this appendix we will review some of the wide variety of available sources on GP which should assist readers who wish to explore further. Consulting information available on the Web is certainly a good way to get quick answers for someone who wants to know what GP is. These answers, however, will often be too shallow for someone who really wants to then apply GP to solve practical problems. People in this position should probably invest some time going through more detailed accounts; some of the key books in the field include (Banzhaf, Nordin, Keller, and Francone, 1998a; Koza, 1992; Langdon and Poli, 2002), and others are listed in Section A.1 . Technical papers in the extensive GP literature may be the next stage. Although this literature is easily accessible thanks to the complete on-line bibliography (Langdon et al., 1995-2008), newcomers will often need to be selective in what they read. The objective here may be different for different types of readers. Practitioners may wish to focus initially on papers which deal with the same problem they are interested in. Researchers and PhD students interested in developing a deeper understanding of GP should also make sure they identify and read as many seminal papers as possible, including papers or books on empirical and theoretical studies on the inner mechanisms and behaviour of GP. These are frequently cited in other papers and, so, can be easily identified.