Using emergent modularity to develop control systems for mobile robots
Adaptive Behavior - Special issue on environment structure and behavior
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Neural control of a modular multi-legged walking machine: Simulation and hardware
Robotics and Autonomous Systems
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Strategies of incremental evolution of artificial neural systems have been suggested over the last decade to overcome the scalability problem of evolutionary robotics In this article two methods are introduced that support the evolution of neural couplings and extensions of recurrent neural networks of general type These two methods are applied to combine and extend already evolved behavioral functionality of an autonomous robot in order to compare the structure-function relations of the resulting networks with those of the initial structures The results of these investigations indicate that the emergent dynamics of the resulting networks turn these control structures into irreducible systems We will argue that this leads to several consequences One is, that the scalability problem of evolutionary robotics remains unsolved, no matter which type of incremental evolution is applied.