Evolution of Neural Networks
Description
Neuroevolution, or optimization of neural networks through evolutionary computation, has gained significant momentum recently. Its primary focus is on evolving neural networks for intelligent agents when the training targets are not known, and good performance requires many decisions over time, such as robotic control, game playing, and decision-making. More recently it has also been extended to optimizing deep-learning architectures, understanding how biological intelligence evolved, and optimizing neural networks for hardware implementation. This tutorial introduces students to the basics of neuroevolution, progresses to several advanced topics that make neuroevolution more effective and more general, reviews example application areas, and proposes further research questions.
Organizers
Risto Miikkulainen
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, and previously as CTO of Sentient Technologies, he is scaling up these approaches to real-world problems. Risto is an IEEE Fellow, recipient of the IEEE CIS EC Pioneer Award, INNS Gabor Award, ISAL Outstanding Paper of the Decade Award, as well as 10 Best-Paper Awards at GECCO.
Sebastian Risi
Sebastian Risi is a Professor at the IT University of Copenhagen where he directs the Creative AI Lab and a Research Director at modl.ai. Sebastian received his PhD from the University of Central Florida in 2012. He has won several international scientific awards, including multiple best paper awards, an ERC Consolidator Grant in 2022, the Distinguished Young Investigator in Artificial Life 2018 award, a Google Faculty Research Award in 2019, and an Amazon Research Award in 2020. His interdisciplinary work has been published in major machine learning, artificial life, and human-computer interaction conferences and has been covered by various media outlets, including Science, New Scientist, Wired, Fast Company, and The Register.
David Ha
David Ha is the Co-founder and CEO of Sakana AI, an AI Lab in Tokyo focused on advancing foundational AI models using the concepts of computational evolution. He previously worked as a Research Scientist at Google, leading the Google Brain Research team in Japan. His research interests include complex systems, self-organization, and creative applications of machine learning. He has published papers in the intersection of Deep Learning, Artificial Life, Neuroevolution and Complex Systems at NeurIPS, ICLR, ICML, GECCO, Artificial Life, Collective Intelligence. Prior to joining Google, he was a derivatives trader, serving as Managing Director and head of interest rates trading at Goldman Sachs in Japan. He obtained his undergraduate in engineering science from the University of Toronto, and a PhD from the University of Tokyo.
Yujin Tang
Yujin Tang is a research scientist at Sakana AI. He received an M.S in Engineering from Waseda University, and a Ph.D. in Computer Science from The University of Tokyo. Before his current job, he worked at Google Brain and later Google DeepMind as a research engineer and a research scientist, during the time of which he published multiple evolutionary computing related papers at GECCO, NeurIPS, Artificial Life, earning best paper and best paper runner-up awards. He also developed and open-sourced EvoJAX – a toolkit for accelerated neuroevolution. His current research focuses on methods of neuroevolution in the applications of improving foundation models.