  Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in
particular are well known tools for successful optimization of difficult
problems. But when is their application meaningful and how does one approach
such a project as a novice? How do you avoid beginner's mistakes or use the
design possibilities of a metaheuristic search as efficiently as possible? This
paper tries to give answers to these questions based on 30 years of research
and application of the Evolutionary Algorithm GLEAM and its memetic extension
HyGLEAM. Most of the experience gathered and discussed here can also be applied
to the use of other metaheuristics such as ant algorithms or particle swarm
optimization. This paper addresses users with basic knowledge of MHs in general
and EAs in particular who want to apply them in an optimization project. For
this purpose, a number of questions that arise in the course of such a project
are addressed. At the end, some non-technical project management issues are
discussed, whose importance for project success is often underestimated.
