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Using evolutionary algorithms for the optimization of the sensorimotor control in an autonomous agent

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Huber,  S
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Mallot,  HA
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Huber, S., Mallot, H., & Bülthoff, H. (1996). Using evolutionary algorithms for the optimization of the sensorimotor control in an autonomous agent. In B. Bouchon-Meunier (Ed.), 6th International Conference Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 1996) (pp. 1241-1246). Granada, Spain: Universidad de Granada.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-EB54-2
Abstract
Autonomous agents that evolve visually-guided control mechanisms using genetic algorithms (GA) and evolutionary strategies (ES) are introduced. These agents are situated in simulated environments and are designed based on the neurobiological principles of various aspects of insect navigation. They generate behavioral modules for obstacle avoidance and the compensation for rotations caused by external disturbances. The sensor positions and the visuomotor coupling evolve with the sensors and motors acting in a closed loop of perception and action. The probabilities for the genetic operations mutation and crossover are optimized and the results of the two optimization techniques genetic algorithms and evolutionary strategies are compared.