Genetic evolution of hierarchical behavior structures
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
Robotics and Autonomous Systems
Hierarchical controller learning in a first-person shooter
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Improving control through subsumption in the evotanks domain
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
PLAZZMID: an evolutionary agent-based architecture inspired by Bacteria and Bees
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
IEEE Transactions on Evolutionary Computation
Evolving modularity in robot behaviour using gene expression programming
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
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An approach to robotics called layered evolution and merging features from the subsumption architecture into evolutionary robotics is presented, and its advantages are discussed. This approach is used to construct a layered controller for a simulated robot that learns which light source to approach in an environment with obstacles. The evolvability and performance of layered evolution on this task is compared to (standard) monolithic evolution, incremental and modularised evolution. To corroborate the hypothesis that a layered controller performs at least as well as an integrated one, the evolved layers are merged back into a single network. On the grounds of the test results, it is argued that layered evolution provides a superior approach for many tasks, and it is suggested that this approach may be the key to scaling up evolutionary robotics.