Heuristics for car setup optimisation in TORCS

A TORCS-based (The Open Racing Car Simulator) car setup optimisation problem requires a search for the best parameter settings of a race car that improves its performance across different types of race tracks. This problem often exhibits a noisy environment due to the properties of the race track as well as the components of the car. Selection hyper-heuristics are methodologies that control and mix different predefined set of heuristics during the search process for solving computationally hard problems. In this study, we represent the car setup problem as a real valued optimisation problem and investigate the performance of different approaches including a set of heuristics and their combination controlled by a selection hyper-heuristic framework. The results show that selection hyper-heuristics and a tuned heuristic perform well and are promising approaches even in a dynamically changing, noisy environment.