Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Evolving mobile robots able to display collective behaviors
Artificial Life
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Neuro-evolution for a gathering and collective construction task
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Designing multi-rover emergent specialization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Collective specialization for evolutionary design of a multi-robot system
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
Evolution of collective behavior in a team of physically linked robots
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
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This paper is a preliminary study of the types of collective behavior tasks that are best solved by Neuro-Evolution (NE). This research tests a hypothesis that for a multi-rover task, the best approach (for deriving effective collective behaviors) is to evolve complete Artificial Neural Network (ANN) controllers, and then combine controller behaviors in a collective behavior context. Such methods are called multi-agent Conventional Neuro-Evolution (Multi-Agent CNE). This is opposed to methods such as Enforced Sub-Populations (ESP) which evolves individual neurons and then combines them to form complete ANN controllers. Single and multi-agent CNE and ESP approaches to evolving collective behavior solutions are tested comparatively in the multi-rover task. The multi-rover task requires that teams of rovers (controllers) cooperate in order to detect features of interest in a virtual environment. Results indicate that a Multi-Agent CNE approach derives rover teams with a higher task performance and genotype diversity, comparative to ESP.