We present an extension of a graph-based evolutionary algorithm called Genetic Network Programming (GNP) by a novel mutation operator, which allows for a variable number of nodes and edges per individual. With this operator, the search space is significantly extended, but without the risk of incurring the bloat problem. The operator is fitness neutral and has no hyper-parameter. Due to higher flexibility, it is now possible for GNP to automatically adapt to the complexity of a given task and to find suitable features, especially for high dimensional data sets. We applied our mutation operator successfully in a GNP for a financial data set where it improved over standard GNP with an optimal network size while maintaining the interpretability of the solution candidates.