Page 161 - 49A Field Guide to Genetic Programming

A.2 Key Journals 147

A.2 Key Journals

In addition to GP’s own Genetic Programming and Evolvable Machines jour-
nal, Evolutionary Computation, the IEEE transaction on Evolutionary Com-
putation, Complex Systems (Complex Systems Publication, Inc.), the new
Journal on Artificial Evolution and Applications and many others publish
GP articles. The GP bibliography (Langdon et al., 1995-2008) lists a further
375 different journals worldwide that have published articles related to GP.

A.3 Key International Meetings

EuroGP – the European Conference on Genetic Programming – has been
held every year since 1998. All EuroGP papers are available on line as part of
Springer’s LNCS series. The original annual Genetic Programming confer-
ence ran for three years (1996-1998) before combining in 1999 with the Inter-
national Conference on Genetic Algorithms (ICGA) to form GECCO. 98%
of GECCO papers are available on-line. The Michigan-based Genetic Pro-
gramming Theory and Practice workshop (O’Reilly, Yu, Riolo, and Worzel,
2004; Riolo and Worzel, 2003; Riolo, Soule, and Worzel, 2007a; Yu, Riolo,
and Worzel, 2005) has recently published its fifth proceedings (Riolo, Soule,
and Worzel, 2007b). Other EC conferences, such as CEC, PPSN, Evolution
Artificielle and WSC, also regularly contain GP papers.

A.4 GP Implementations
One of the reasons behind the success of GP is that it is easy to implement
own versions, and implementing a simple GP system from scratch remains
an excellent way to make sure one really understands the mechanics of GP.
In addition to being an exceptionally useful exercise, it is often easier to
customise (e.g., adding new, application specific genetic operators or imple-
menting unusual, knowledge-based initialisation strategies) a system one has
built for new purposes than a large GP distribution. All of this, however,
requires reasonable programming skills and the will to thoroughly test the
resulting system until it behaves as expected.
This is actually an extremely tricky issue in highly stochastic systems
such as GP, as we discussed in Section 13.1. The problem is that almost
any system will produce “interesting” behaviour, but it is typically very
hard to test whether it is exhibiting the correct interesting behaviour. It
is remarkably easy for small mistakes to go unnoticed for extended periods
of time (even years). 2 It is also easy to incorrectly assume that “minor”
2
Several years ago Nic and some of his students discovered that one of their systems
had been performing addition instead of subtraction for several months due to a copy-