The dynamics of the best individuals in co-evolution

There continues to be a growing interest in the use of co-evolutionary algorithms to solve difficult computational problems. Their performance however has varied widely from good to disappointing. The main reason for this is that co-evolutionary systems can display quite complex phenomena. Therefore, in order to efficiently use co-evolutionary algorithms for problem solving, one must have a good understanding of their dynamical behavior. To build such understanding, we have constructed a methodology for analyzing co-evolutionary dynamics based on trajectories of best-of-generation individuals. We applied this methodology to gain insights into how to tune certain algorithm parameters in order to improve performance