Computational optimization methods can broadly be classified into two
groups: classical methods, which require and exploit specific
functional forms of objective function and constraints, and
heuristics.  Those latter methods impose few, if any, restrictions on
models, at the price of being more computationally demanding.  But
because of the growth of computing capacity over the last decades,
those methods are now perfectly-practical tools for everyday use.
Yet, instead of realizing the advantages of heuristics, users still
cling to classical methods.  We discuss the reasons for this
non-acceptance of heuristics, and argue that the choice of
numerical-optimization techniques is as much driven by the culture of
the user -- field of work and educational background -- as by the
quality of the method.  In particular, we argue that many of the
alleged shortcomings of heuristics could be overcome if researchers
stopped treating optimization as a mathematical, exact discipline;
instead, they should consider it a practical/computational tool.