Overtaking Uncertainty With Evolutionary TORCS Controllers: Combining BLX With Decreasing $\alpha$ Operator and Grand Prix Selection

Evolution is a powerful problem-solving technique, extensively used for designing racing car controllers, but with a series of challenges: an evaluation function that can separate the best controllers from the rest, and a series of operators that can explore different possibilities in the controller search space. Within the context of the TORCS racing simulator, in this article, we introduce a selection policy based on competition called
Grand Prix Selection
(GPS), which will be able to increase robustness by using something more realistic than solo race scores to select individuals. Additionally, we increase the exploitative power of this kind of selection via a BLX operator with continuously decreasing
$alpha$
. We compare these new selection and operator with hybrid approaches that apply GPS only part of the time, as well as other classical crossover operators. In general, experiments show that these combined improvements establish a new level of performance of evolved controllers, being able to beat both standard and previously evolved ones, as well as a high-ranked controller of TORCS competitions.