Neural Computation
Evolving neural networks through augmenting topologies
Evolutionary Computation
Neuroevolution of an automobile crash warning system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior
Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior
Robust player imitation using multiobjective evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving driving controllers using genetic programming
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolving driving controllers using genetic programming
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Driving faster than a human player
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Learning, evolution and adaptation in racing games
Proceedings of the 9th conference on Computing Frontiers
An evolutionary tuned driving system for virtual car racing games: The AUTOPIA driver
International Journal of Intelligent Systems
Evolutionary optimization of a neural network controller for car racing simulation
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Advanced overtaking behaviors for blocking opponents in racing games using a fuzzy architecture
Expert Systems with Applications: An International Journal
Car setup optimization via evolutionary algorithms
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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The TORCS simulated car racing competition was introduced over a year ago. It asks for the design of racing car control strategies that have to rely on local track and driving information only, such as distance sensors, angle-to-track axis, or velocity vectors. Thus, the competition asks for strategies that are sensory-motorically grounded rather than strategies that can be designed (online or even offline) by an external observer that has full track knowledge. Moreover, the competition enforces the development of rather general driving strategies since optimization is on driving success in general rather than driving success on one particular track. This paper describes the steps taken to develop COBOSTAR, an autonomous racing car strategy with several general, context-dependent behavioral modules and strategic advancements. Most of the behavioral parameters were optimized with covariance matrix adaptation evolutionary strategy techniques. COBOSTAR won the simulated car racing competition at the IEEE Congress of Evolutionary Computation (CEC 2009) and there is still lots of room for further optimizations and strategy additions. Apart from describing the COBOSTAR racer in detail, we also outline possible next steps and future challenges.