Applying cooperative coevolution to compete in the 2009 TORCS Endurance World Championship

The TORCS Endurance World Championship is an international competition in which programmers develop and tune their drivers to race against each other using TORCS, a state-of-the-art car racing simulator. In this work, we applied evolutionary computation to develop a driver for the 2009 edition of this competition. In particular, we focused on the optimization of the car setup of an existing driver (the winner of the 2008 edition) and applied cooperative coevolution to evolve the best car setup for the qualifying rounds of each leg of the championship. After 10 legs involving 12 teams, our driver was able to reach the 4th position in the final standings. We believe that this is a very promising result especially if we consider that we only focused on the car setup and the other teams participated also to most of the previous four editions (gaining much domain knowledge). Overall, our results show that cooperative coevolution can be very effective in this complex optimization task producing setups that can be competitive with respect to the ones tuned by human experts. Therefore, our results also suggest that simple evolutionary computation might represent a helpful tool to human designers for improving the performance of already heavily tuned drivers.