  We have employed a recent implementation of genetic algorithms to study a
range of standard benchmark functions for global optimization. It turns out
that some of them are not very useful as challenging test functions, since they
neither allow for a discrimination between different variants of genetic
operators nor exhibit a dimensionality scaling resembling that of real-world
problems, for example that of global structure optimization of atomic and
molecular clusters. The latter properties seem to be simulated better by two
other types of benchmark functions. One type is designed to be deceptive,
exemplified here by Lunacek's function. The other type offers additional
advantages of markedly increased complexity and of broad tunability in search
space characteristics. For the latter type, we use an implementation based on
randomly distributed Gaussians. We advocate the use of the latter types of test
functions for algorithm development and benchmarking.
