The Racing Algorithm: Model Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Proceedings of the 6th International Conference on Genetic Algorithms
Innately adaptive robotics through embodied evolution
Autonomous Robots
Embodied Evolution with a New Genetic Programming Variation Algorithm
ICAS '08 Proceedings of the Fourth International Conference on Autonomic and Autonomous Systems
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Embodied evolution and learning: the neglected timing of maturation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
On-line, on-board evolution of robot controllers
EA'09 Proceedings of the 9th international conference on Artificial evolution
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The balance between initial training and lifelong adaptation in evolving robot controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In evolutionary robotics, robot controllers are often evolved in a separate development phase preceding actual deployment - we call this off-line evolution. In on-line evolutionary robotics, by contrast, robot controllers adapt through evolution while the robots perform their proper tasks, not in a separate preliminary phase. In this case, individual robots can contain their own self-sufficient evolutionary algorithm (the encapsulated approach) where individuals are typically evaluated by means of a time sharing scheme: an individual is given the run of the robot for some amount of time and fitness corresponds to the robot's task performance in that period. Racing was originally introduced as a model selection procedure that quickly discards clearly inferior models. We propose and experimentally validate racing as a technique to cut short the evaluation of poor individuals before the regular evaluation period expires. This allows an increase of the number of individuals evaluated per time unit, but it also increases the robot's actual performance by virtue of abandoning controllers that perform inadequately. Our experiments show that racing can improve the performance of robots that adapt their controllers by means of an on-line evolutionary algorithm significantly.