Evolving Artificial Neural Networks that Develop in Time
Proceedings of the Third European Conference on Advances in Artificial Life
Evolving Robot Behaviours with Diffusing Gas Networks
Proceedings of the First European Workshop on Evolutionary Robotics
IEEE Transactions on Neural Networks
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In this paper, we propose a phenomenological developmental model based on a stochastic evolutionary neuron migration process (SENMP). Employing a spatial encoding scheme with lateral interaction of neurons for artificial neural networks representing candidate solutions within a neural network ensemble, neurons of the ensemble form problem-specific geometrical structures as they migrate under selective pressure. The SENMP is applied to evolve purposeful behaviors for autonomous robots and to gain new insights into the development, adaptation and plasticity in artificial neural networks. We demonstrate the feasibility and advantages of the approach by evolving a robust navigation behavior for a mobile robot. We also present some preliminary results regarding the behavior of the adapting neural network ensemble and, particularly, a phenomenon exhibiting Hebbian dynamics.