1. Car setup optimization via evolutionary algorithms
Carlos Cotta,
Antonio J. Fern´andez-Leiva,
Alberto Fuentes S´anchez,
Ra´ul Lara-Cabrera
Dept. Lenguajes y Ciencias de la
Computaci´on, University of M´alaga,
SPAIN
http://anyself.wordpress.com
http://dnemesis.lcc.uma.es

2. Introduction
Artificial intelligence (AI) in games has become a very important
research field
International conferences and journals that only focus on this
topic: CIG, AIIDE, TCIAIG
Games offer a large variety of AI research problems: planning,
player modeling, decision making under uncertainty, ...
They should be used as tool for testing AI techniques
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3. TORCS: The Open Racing Car Simulator
Open-source 3D racing simulator
Human and artificial players (bots)
Client-server architecture:
Bots run as an external process
Communication with the race server through an UDP connection
Cars have 50 mechanical parameters:
Tyre angles, suspension’s hardness, ...
Good testing framework for optimization techniques
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4. The competition
The contest involves three tracks
The objective is to find the best car setup for each one of the
tracks
Two phases: optimization and evaluation (time-limited)
A car setup is represented by a vector of real numbers (50
parameters)
Participants are ranked according to their maximum covered
distance
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5. Steady-state approach (I)
Parameters are real values and
encoded with 10-bit
Each individual of the
population is an array of 500
bits
Crossover and mutation with
probability 1.0
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6. Steady-state approach (II)
Fitness function
C1 ∗ distraced + C2 ∗ topspeed + C3 ∗ (1000 − bestlap) + C4 ∗ damage
distraced Total amount of distance
topspeed Maximum speed
bestlap Best lap time
damage Damage taken by the car
Several combinations of weights C1, C2, C3, C4 have been tested.
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7. Steady-state approach (III)
Experimental Analysis
Runs:10 Population:50 Iterations:20
Best weights after testing several combinations: C1 = 0.6,
C2 = 2.5, C3 = 0.15 and C4 = 0.05
Controller submitted to the EVO-* competition:
Competitor CG Track Poli-Track Dirt-3 Distance Points
Mu˜noz (MOEA) 10 6 8 23614.13 24
Garc´ıa-S´aez (PSO) 6 10 5 21388.04 21
Walz (PSO) 8 5 6 21049.77 19
Fuent-Cotta-Fdez-Cab (GA) 4 4 10 19748.08 18
Mu˜noz-Mart´ın-S´aez (EA) 5 8 4 20515.29 17
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8. Multi-objective approach
Multi-objective algorithm using SPEA2
We have tested several combinations of fitness functions:
Variables: bestlap, distraced, damage, topspeed and the fitness
defined for the single-objective algorithm
Best results obtained from two objectives: minimize the time of the
best lap and maximize the single-objective fitness
Additionally, we have considered the optimization of every variable,
that is, maximize distraced and topspeed and minimize bestlap and
damage
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9. Multi-objective approach (II)
Experimental Analysis
Runs:10 Population:50 Generations:20
Compared to the participants of the competition held at
GECCO-2009
Driver Speedway ETRACK Olethros Wheel Total
Multi-objective 10 5 8 8 31
V&M&C 4 8 5 10 27
Jorge 8 4 10 4 26
Multi-objective PCA 3 10 6 6 25
Single-objective 5 6 4 5 20
Luigi 6 3 3 3 15
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10. Conclusions
Different proposals based on evolutionary computation to set up a
car in a racing simulator
Multi-objective evolutionary algorithms are a good solution to the
problem
The single-objective algorithm has determined the fitness function
used in our EMOAs
Future work:
Use meta-optimization to get a better fitness function
Improve evolutionary algorithms’ parameters in order to obtain better
results
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11. Thanks for your attention!
AnySelf Project
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@anyselfproject
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@DNEMESISproject
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