The minimization problem of a nonconvex function of multiple variables is a fundamental mathematical problem in soft computing, and a variety of solutions are presented in the scientific literature. Genetic algorithms belong to the field of computer science and comprise a method of search for optimal solutions in systems that can be described as a mathematical problem. It is useful in problems containing a high number of parameters/dimensions. In particular, they apply in cases when no analytical method exists for the finding of the optimal combination of values for the variables that the system reacts as best as possible according to a defined objective. The method of Genetic Algorithms is inspired by biology. It uses the idea of evolution by a genetic mutation, natural selection, and crossover. Genetic Algorithms are quite easy to be developed. The values for the parameters of the system must be coded to be represented by a variable that contains a 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 the 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. **All corporate names and trademarks are the property of their respective companies.*