Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolving neural networks through augmenting topologies
Evolutionary Computation
Learning to Race: Experiments with a Simulated Race Car
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Neuroevolution of an automobile crash warning system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
ACM SIGEVOlution
The 2007 IEEE CEC simulated car racing competition
Genetic Programming and Evolvable Machines
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Computing with the social fabric: The evolution of social intelligence within a cultural framework
IEEE Computational Intelligence Magazine
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Learning drivers for TORCS through imitation using supervised methods
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Interactive evolution for the procedural generation of tracks in a high-end racing game
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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
Advanced overtaking behaviors for blocking opponents in racing games using a fuzzy architecture
Expert Systems with Applications: An International Journal
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Modern computer games are at the same time an attractive application domain and an interesting testbed for the evolutionary computation techniques. In this paper we apply NeuroEvolution of Augmenting Topologies (NEAT), a well known neuroevolution approach, to evolve competitive non-player characters for a racing game. In particular, we focused on The Open Car Racing Simulator (TORCS), an open source car racing simulator, already used as a platform for several scientific competitions dedicated to games. We suggest that a competitive controller should have two basic skills: it should be able to drive fast and reliably on a wide range of tracks and it should be able to effectively overtake the opponents avoiding the collisions. In this paper we apply NEAT to evolve separately these skills and then we combined them together in a single controller. Our results show that the resulting controller outperforms the best available controllers on a challenging racing task. In addition, the experimental analysis also confirms that both the skills are necessary to develop a competitive controller.