Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Inductive functional programming using incremental program transformation
Artificial Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Machine Learning
Learning to Race: Experiments with a Simulated Race Car
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Online speed adaptation using supervised learning for high-speed, off-road autonomous driving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using automatic programming to generate state-of-the-art algorithms for random 3-SAT
Journal of Heuristics
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A variety of machine learning techniques have been employed to automatically create control algorithms for autonomous vehicles. Much research has focused on various "black box" approaches, in which the synthesized or learned control algorithms perform well when tested, but are difficult or impossible to analyze and understand. This paper presents the use of the ADATE system to evolve a control algorithm based on a racing car simulator. The system evolved compact and analyzable yet sophisticated control algorithms capable of driving millions of randomly generated tracks at high speeds without ever driving off the road. The approach presented is likely to be applicable to most automatic control problems, given a set of training examples and a suitable software simulator.