Part of the future work proposed in the degree work Method for the synthesis of two-dimensional optimization landscapes by Mariana Medina is addressed. The process of generating optimizer test problems through the implementation of a VAE is addressed, also proposing the process of characterization of the problems generated through the use of symbolic regressors. The experimental design is carried out on the variations in the number of dimensions that compose the latent space, the symbolic regressor PySR is considered the winner, and finally, the generated and generated databases are compared through the use of a self-organizing map (SOM) trained by means of the use of the ELA characteristics of the generated optimization problems. High-dimensional generation and controlled generation are proposed as future work. The work performed contributes to the development of the paper Optimization test function synthesis with generative adversarial networks and adaptive neuro-fuzzy systems.