Regularized radial basis functional networks: theory and applications
Regularized radial basis functional networks: theory and applications
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
On Decentralizing Selection Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Embodiment of Evolutionary Computation in General Agents
Evolutionary Computation
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
Embodied evolution and learning: the neglected timing of maturation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
The balance between initial training and lifelong adaptation in evolving robot controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Adaptively Coordinating Heterogeneous Robot Teams through Asynchronous Situated Coevolution
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Self-organizing robot teams using asynchronous situated co-evolution
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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In this paper we present an evolutionary method that can deal with the specific problem requirements of adaptivity, scalability and robustness. These requirements are increasingly observed in the areas of pervasive and autonomic computing, and the area of collective robotics. For the purpose of this paper, we concentrate on the problem domain of collective robotics, and more specifically on a surveillance task for such a collective. We present the Situated Evolution Method as a viable alternative for classical evolutionary methods specifically for problem domains with the aforementioned requirements. By means of simulation experiments for a surveillance task, we show that our new method does not lose performance in comparison with a classical evolutionary method, and it has the important design and deployment advantage of being adaptive, scalable and robust.