Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Autonomous Robots
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
Proceedings of the Third European Conference on Advances in Artificial Life
Evolving mobile robots able to display collective behaviors
Artificial Life
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Evolving keepaway soccer players through task decomposition
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Collective specialization in multi-rover systems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Designing multi-rover emergent specialization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Neuro-evolution approaches to collective behavior
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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This research is positioned in the context of controller design for (simulated) multi-robot applications. Inspired by research in survey and exploration of unknown environments where a multi-robot system is to discover features of interest given strict time and energy constraints, we defined an abstract task domain with adaptable features of interest. Additionally, we parameterized the behavioral features of the robots, so that we could classify behavioral specialization in the space of these parameters. This allowed systematic experimentation over a range of task instances and types of specialization in order to investigate the advantage of specialization. These experiments also delivered a novel neuro-evolution approach to controller design, called the collective specialization method. Results elucidated that this method derived multirobot system controllers that outperformed a high performance heuristic and conventional neuro-evolution method.