Abstract: Neural network weights and topologies were originally evolved in order to solve tasks where gradients are not available. Recently, it has also become a useful technique for metalearning architectures of deep learning networks. However, neuroevolution is most powerful when it utilizes synergies of evolution and learning. In this talk I review several examples of such synergies: evolving loss functions, activation functions, surrogate optimization, and human-designed solutions. I will demonstrate these synergies in image recognition, game playing, and pandemic policy optimization, and point out opportunities for future work.
Speaker: Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and Associate VP of Evolutionary AI at Cognizant. He received an M.S. in Engineering from Helsinki University of Technology (now Aalto University) 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 vision; he is an author of over 450 articles in these research areas. At Cognizant, he is scaling up these approaches to real-world problems. Risto is an IEEE Fellow; his work on neuroevolution has recently been recognized with the IEEE CIS Evolutionary Computation Pioneer Award, the Gabor Award of the International Neural Network Society and Outstanding Paper of the Decade Award of the International Society for Artificial Life.