Gene Regulatory Networks (GRNs) describe the interactions between different genes. One of the most important tasks in biology is to find the right regulations in a GRN given observed data. The problem, is that the data is often noisy and scarce, and we have to use models robust to noise and scalable to hundreds of genes. Recently, Recursive Neural Networks (RNNs) have been presented as a viable model for GRNs, which is robust to noise and can be scaled to larger networks. In this paper, to optimize the parameters of the RNN, we implement a classic Population Based Incremental Learning (PBIL), which in certain scenarios has outperformed classic GA and other evolutionary techniques like Particle Swarm Optimization (PSO). We test this implementation on a small and a large artificial networks. We further study the optimal tunning parameters and discuss the advantages of the method.