One of the main challenges of computer science is to get a computer execute a task that must be done, without telling it how to do it. Genetic Programming (GP) deals with this challenge from a high level statement of what is needed to be done and creates a computer program to solve the problem automatically. In this dissertation we developed an extension of Quantum-Inspired Linear Genetic Programming Model (QILGP), aiming to improve its efficiency and effectiveness in the search for solutions. For this, first the algorithm is structured in a Heterogeneous Parallelism System, Aiming to accelerated using Graphics Processing Units GPU and multiple CPU processors, reducing the timing of data transfers while maximizing the speed of the processes. Second, using the techniques of Graphic Visualization which interpret the structure and the processes that the algorithm evolves, understanding the behavior of QILGP. We used the highperformance features such as Message Passing Interface (MPI) and Open Multi- Processing (OpenMP), which are of vital importance when working with multiprocesses, as it is necessary to design a topology that has multiple levels of parallelism to avoid delaying the process for transferring the data to a local computer where the visualization is projected. In addition to graphically represent the parameters of PGLIQ devising the behavior over generations, a 3D visualization for cases of evolutionary robotics is presented, in which the tools of dynamic simulation as Bullet SDK and graphics engine OGRE for rendering are used . This visualization is used as a tool for a case study in this dissertation.