The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents
The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Levels of dynamics and adaptive behavior in evolutionary neural controllers
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Evolving 3d morphology and behavior by competition
Artificial Life
Evolution of neural organization in a hydra-like animat
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
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Body morphology is thought to have heavily influenced the evolution of neural architecture. However, the extent of this interaction and its underlying principles are largely unclear. To help us elucidate these principles, we examine the artificial evolution of a hypothetical nervous system embedded in a fish-inspired animat. The aim is to observe the evolution of neural structures in relation to both body morphology and required motor primitives. Our investigations reveal that increasing the pressure to evolve a wider range of movements also results in higher levels of neural symmetry. We further examine how different body shapes affect the evolution of neural structure; we find that, in order to achieve optimal movements, the neural structure integrates and compensates for asymmetrical body morphology. Our study clearly indicates that different parts of the animat - specifically, nervous system and body plan - evolve in concert with and become highly functional with respect to the other parts. The autonomous emergence of morphological and neural computation in this model contributes to unveiling the surprisingly strong coupling of such systems in nature.