Empirical review of standard benchmark functions using evolutionary global optimization

Understand

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.