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The rapid advances in the field of optimization methods in many pure andapplied science pose the difficulty of keeping track of the developments aswell as selecting an appropriate technique that best suits the problem in-hand.From a practitioner point of view is rightful to wander "which optimizationmethod is the best for my problem?". Looking at the optimization process as a"system" of intercon- nected parts, in this paper are collected some ideasabout how to tackle an optimization problem using a class of tools fromevolutionary computations called Genetic Algorithms. Despite the number ofoptimization techniques available nowadays the author of this paper thinks thatGenetic 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 thenon-computer-scientist that has to deal with optimization problems both in thescience and engineering practice. No original methods or algorithms areproposed.

Between theory and practice: guidelines for an optimization scheme with genetic algorithms - Part I: single-objective continuous global optimization