  The rapid advances in the field of optimization methods in many pure and
applied science pose the difficulty of keeping track of the developments as
well as selecting an appropriate technique that best suits the problem in-hand.
From a practitioner point of view is rightful to wander "which optimization
method is the best for my problem?". Looking at the optimization process as a
"system" of intercon- nected parts, in this paper are collected some ideas
about how to tackle an optimization problem using a class of tools from
evolutionary computations called Genetic Algorithms. Despite the number of
optimization techniques available nowadays the author of this paper thinks that
Genetic Algorithms still play a central role for their versatility, robustness,
theoretical framework and simplicity of use. The paper can be considered a
"collection of tips" (from literature and personal experience) for the
non-computer-scientist that has to deal with optimization problems both in the
science and engineering practice. No original methods or algorithms are
proposed.
