A numerical investigation of adaptation in populations of random boolean networks

We investigate the adaptation of random boolean networks that are a model for regulatory gene networks. The model considers a general genetic algorithm and a fitness function that takes into account the full network dynamical behavior. We propose a mathematical function to quantify the complexity catastrophe. We also find that the latter occurs when the task complexity increases, i.e., using networks with longer periods. Finally, we discuss a scenario that describes the adaptation on the proposed fitness landscape.