Of all the types of algorithms in existence, some have the particularity of being inspired by the evolution of species in their natural setting. These are genetic algorithms. Species adapt to their living environment, which may evolve; individuals of each species reproduce, creating new individuals; some undergo DNA modifications; some disappear. In this paper, we explore the world of genetic algorithms, highlighting their principle, their mode of use, their relationship with other machine-learning techniques and their place in the world of decision-making characterized by the emergence of data science and data analytics.