Evolution of Neural Networks

Friday, March 24, 2017, 11:00 am - 12:00 pm PSTiCal
11th floor large conference room
This event is open to the public.
Risto Miikkulainen




Evolution of artificial neural networks has recently emerged as a powerful technique both in deep networks and reinforcementlearning. While the performance of deep learning networks depends crucially on the network architecture; with neuroevolution, it ispossible to discover such architectures automatically. While reinforcement learning works well when the environment is fully observable, neuroevolution makes it possible to disambiguate hidden state through memory. In this tutorial, I will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining gradient-based training with evolutionary methods, and (3) applications of neuroevolution to control, robotics, artificial life, and games.


Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and a Research Fellow at Sentient Technologies, Inc. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990.  His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing, and self-organization of the visual cortex; he is an author of over 370 articles in these research areas.  He is an IEEE Fellow, member of the Board of Governors of the Neural Network Society, and an action editor of Cognitive Systems Research and IEEE Transactions on Computational Intelligence and AI in Games.

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