An elementary mathematical problem in soft computing is the minimization problem of a non-convex function of many variables. Genetic algorithms belong to the field of computer science and are a method of search for optimal solutions in systems that can be described as a mathematical problem. It is useful in problems containing many parameters / dimensions. In particular when no analytical method is able to find the optimal combination of values for the variables that the test system reacts as possible with the desired way. The method of Genetic Algorithms is inspired by biology. It uses the idea of evolution by genetic mutation, natural selection, and crossover. The Genetic Algorithms are quite easy to develop. The values for the parameters of the system must be coded so be represented by a variable that contains character or bit string (0/1). This variable mimics the genetic code that exists in living organisms. Initially, the Genetic Algorithm produces multiple copies of variable / genetic code, usually by random values, generating a population of solutions. Each solution (values for the parameters of the system) is tested for how close it brings the reaction of the system to the desired through a function that gives the capacity measure of the solution, which is called objective function. The following videos demonstrate examples, of the implementation of Genetic Algorithms to the structural analysis software SAP2000, ETABS and StereoSTATIKA, for size, topology, mixed as well as dampers optimization.