Trading-off Mind Complexity and Locomotion in a Physically Simulated Quadruped
نویسندگان
چکیده
This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a pareto optimal set of artificial neural networks that optimizes the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. Here we also analyze the evolutionary dynamics of controller evolution.
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Trading-off Mind Complexity and Locomotion in Virtually Embodied Quadruped Robot
This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Paretofrontier Differential Evolution (PDE) algorithm is used to generate a pareto optimal set of artificial neural networks that optimizes the confl...
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