Abstract This paper describes the simulated car racing competition that was

in the championship. The orga-nizers provide short summaries of the other competitors. Finally, we summarize the championship results, followed by a discussion about what the organizers learned about 1) the development of high-performing car racing controllers and 2) the organization of scientific competitions.

this paper is what sort of information a control mechanism needs in order to proficiently race a car around a track, and how it should be represented. Along the way we will look at whether different input representations and control mechanisms give rise to qualitatively different driving styles

Abstract — This paper describes the simulated car racing competition held in association with the IEEE WCCI 2008 conference. The organization of the competition is described, along with the rules, the software used, and the submitted car racing controllers. The results of the competition

Abstract—This paper describes and further extends the fuzzy inferencing system which won the simulated car racing compe-tition that was arranged as part of FuzzIEEE 2007 conference. The details of the winning non-stationary fuzzy controller and its results are presented. A novel approach to further

Several different controller representations are compared on a non-trivial problem in simulated car racing, with respect to learning speed and final fitness. The controller representations are based either on Neural Networks or Genetic Programming, and also differ in regards to whether they allow

Abstract This paper describes the simulated car racing competition that was

Abstract — This paper presents a controller for the 2010 Simulated Car Racing Championship. The idea is not to create the fastest controller but a human-like controller. In order to achieve this, first we have created a process to build a model of the tracks while the car is running and then we

and tackle a complex and decomposable problem. We use the Fuzzy-IEEE 2007 Car Racing Competition as an example of a problem that allows to show the benefits of our approach. In the example presented the results obtained are better and the flexibility is increased with respect to previous works.

Abstract — Multi-objective optimisation is applied to encourage the effective use of state variables in car controlling programs evolved using Genetic Programming. Three different metrics for evaluating the use of state within a program are introduced. Comparisons are performed among multi