The document summarizes the results of a car setup optimization competition. It describes that the goal was for algorithms to optimize car parameters to achieve the best performance on unknown tracks. Five new algorithms were submitted and tested on three tracks, with the winner being a multi-objective evolutionary algorithm from Universidad Carlos III de Madrid. The organizers' algorithms performed well, with a simple genetic algorithm and CMA-ES achieving the top results. While the competition saw varied winners on different tracks, there remains room for improvement over the organizers' approaches.

1. & TU Dortmund
Car Setup Optimization Competition
@ EVOStar 2010
Luigi Cardamone, Daniele Loiacono,
Markus Kemmerling, Mike Preuss
Car Setup Optimization @ EVOStar-2010

2. What is the competition about?
 The goal is to submit an optimization algorithm for a
challenging problem
 The problem: find the best car setup that achieves the
highest performance on a previously unknown track
 The submitted algorithms will be compared on three different
tracks
TU Dortmund
Car Setup Optimization @ EVOStar-2010

3. Optimizing Car Setup in
TORCS

4. Optimizing Car Setup
As in real racing competition, finding a good car racing setup is
a very challenging problem.
 The best set of parameters
depends on:
The track
The driver’s style
(in this case AI controller)
The final measure
 Challenges:
Limited amount of time available for the optimization
Evaluation times must be determined by the algorithm
Noisy fitness function
TU Dortmund
Car Setup Optimization @ EVOStar-2010

5. Which parameters?
 A car presents many parameters that can be optimized:
Gear ratios
Rear/Front wing angle
Brakes
Rear differential
Rear/Front anti-roll bars
Wheels
• Ride
• Toe
• Camber
Suspensions
• Spring
• Bell crank
…
TU Dortmund
Car Setup Optimization @ EVOStar-2010

6. Optimization Framework
 The competition framework creates a physical separation between
the optimizer and the TORCS engine:
Freedom to choose the programming language of the optimizer
Restrict the access only to a set of parameters defined by the
designer
Parameter
Optimization sequence
TORCS
Algorithm
Client Server
UDP
Fitness
evaluation
TU Dortmund
Car Setup Optimization @ EVOStar-2010

7. The optimization process
 The parameters involved in the optimization are normalized
in the range [0,1]
 The optimizer iteratively asks to the TORCS server to
evaluate a given sequence of parameters
 The optimizer must also specify the duration of the each
evaluation (specified in game tics)
 Each time an evaluation request arrives, the server:
1. Plug in the parameters in the car setup
2. A programmed policy (Berniw2) drives the car for the
specified amount of time
3. Finally, the evaluation results are returned as: lap time,
distance raced, top speed and damages
TU Dortmund
Car Setup Optimization @ EVOStar-2010

8. Submissions

9. The competitors
 Five new entries have been submitted to the competition:
Jorge Muñoz - Universidad Carlos III de Madrid, Spain -
MOEA
Moisés Martínez Muñoz, Emilio Martín Gallardo, Yago Saez
Achaerandio – Universidad Carlos III de Madrid, Spain –
(12+10)-EA
Pablo José García Evans, Yago Saez Achaerandio –
Universidad Carlos III de Madrid, Spain – PSO
Wolfgang Walz, TU Dortmund, Germany - PSO
Antonio J. Fernández, Carlos Cotta, Alberto Fuentes –
University of Malaga, Spain
 Entries (not ranked) of the organizers:
Basic Genetic Algorithm (Luigi Cardamone et al., Politecnico di
Milano)
CMA-ES with well chosen parameters (Markus Kemmerling, TU
Dortmund)
TU Dortmund
Car Setup Optimization @ EVOStar-2010

10. The Results

11. Scoring process
 Scoring process involved three tracks (unknown to the
competitors):
Dirt 3 (a dirty track)
Poli-Track (unknown)
CG Track 2 (rather easy and fast)
 For each track, 10 runs of each optimization technique have
been performed
 Each run is stopped after that 1.000.000 of game tics expires
(approximately 2 minutes of computation)
 The champion of each run was evaluated scoring the
distance raced in the first 10.000 game tics of a race
TU Dortmund
Car Setup Optimization @ EVOStar-2010

12. 2010 Tracks Pictures
Dirt-3
Poli-track
CG track 2
TU Dortmund
Car Setup Optimization @ EVOStar-2010

13. CG track 2
12000
10000
8000
6000
4000
2000
avg
0
var
TU Dortmund
Car Setup Optimization @ EVOStar-2010

14. Poli-track
9000
8000
7000
6000
5000
4000
3000
2000
1000 avg
0
var
TU Dortmund
Car Setup Optimization @ EVOStar-2010

15. Dirt-3
7000
6000
5000
4000
3000
2000
1000
avg
0
var
TU Dortmund
Car Setup Optimization @ EVOStar-2010

16. And now some Points (as in Formula-1):
Competitor CG track 2 Poli-track Dirt-3 Overall
Munoz-MOEA 10
Garcia/Saez-PSO 6
Walz-PSO 8
Fernandez/Cotta/Fuentes 4
Munoz/Martin/Saez-EA 5
TU Dortmund
Car Setup Optimization @ EVOStar-2010

17. And now some Points (as in Formula-1):
Competitor CG track 2 Poli-track Dirt-3 Overall
Munoz-MOEA 10 6
Garcia/Saez-PSO 6 10
Walz-PSO 8 5
Fernandez/Cotta/Fuentes 4 4
Munoz/Martin/Saez-EA 5 8
TU Dortmund
Car Setup Optimization @ EVOStar-2010

18. And now some Points (as in Formula-1):
Competitor CG track 2 Poli-track Dirt-3 Overall
Munoz-MOEA 10 6 8
Garcia/Saez-PSO 6 10 5
Walz-PSO 8 5 6
Fernandez/Cotta/Fuentes 4 4 10
Munoz/Martin/Saez-EA 5 8 4
TU Dortmund
Car Setup Optimization @ EVOStar-2010

19. And now some Points (as in Formula-1):
Competitor CG track 2 Poli-track Dirt-3 Overall
Munoz-MOEA 10 6 8 24
Garcia/Saez-PSO 6 10 5 21
Walz-PSO 8 5 6 19
Fernandez/Cotta/Fuentes 4 4 10 18
Munoz/Martin/Saez-EA 5 8 4 17
And the winner is:
Jorge Muñoz - MOEA
Universidad Carlos III de Madrid, Spain
TU Dortmund
Car Setup Optimization @ EVOStar-2010

20. ……and what about the organizers?
Competitor CG track 2 Poli-track Dirt-3 Overall
Munoz-MOEA 9831.83 7654.01 6128.29 23614.13
Garcia/Saez-PSO 8386.77 7979.86 5021.41 21388.04
Walz-PSO 8408.35 7304.54 5336.88 21049.77
Fernandez/Cotta/Fuentes 7553.21 5931.47 6263.40 19748.08
Munoz/Martin/Saez-EA 8167.60 7718.36 4629.33 20515.29
Cardamone-SimpleGA 9563.08 7273.06 5932.09 22768.23
Kemmerling-CMA_ES 10410.13 8392.49 6415.87 25218.49
TU Dortmund
Car Setup Optimization @ EVOStar-2010

21. Conclusions
 Good news:
We had 5 new entries! Wow!
The center of car optimization in Europe is in Spain :-)
 And:
Every track was won by a different algorithm
The winner algorithm is multi-objective and uses all 4
available return values:
top speed, distanced raced, damage and lap time
3rd to 5th place very tight…
 But:
It is obviously not that easy to beat the SimpleGA
Organizer entries show that there is still some potential
TU Dortmund
Car Setup Optimization @ EVOStar-2010