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cs.NE2023
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
Nathan Haut, Wolfgang Banzhaf, Bill Punch
This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can…
cs.RO2023
Discovering Adaptable Symbolic Algorithms from Scratch
Stephen Kelly, Daniel S. Park, Xingyou Song +8
Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based…