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September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Evolving neural networks through neuroevolution

Kenneth Stanley (Uber AI Labs | University of Central Florida)
11:55am–12:35pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial B
Secondary topics:  Algorithms, Deep learning, Tools and frameworks, Transportation and autonomous vehicles
Average rating: ****.
(4.62, 8 ratings)

Prerequisite Knowledge

  • A general understanding of using algorithms for deep learning

What you'll learn

  • Understand the basics of the field of neuroevolution, including motivation for the field, leading algorithms, major applications, software platforms, top research questions, broader implications, and how to get started


Kenneth Stanley offers an overview of the field of neuroevolution, an emerging paradigm for training neural networks through evolutionary principles that has grown up alongside more conventional deep learning. While deep learning focuses on how brain-like structures in computers can learn, neuroevolution addresses how they evolve in the first place, from their architectures to their intrinsic learning dynamics. As in the broader field of deep learning, increases in available computation have led to a renaissance in potential applications of neuroevolution, some of which complement more conventional techniques by offering a path to novel architectures, while others reveal intriguing alternative systems of incentives for learning (even when a gradient is not available).

Neuroevolution offers a rich and unique history of exploring creative and divergent algorithms. Kenneth introduces key algorithms, explains their history and motivations, and shares insight into the kinds of applications they enable. Along the way, he touches on available platforms and software packages and potential links to other deep learning frameworks.

Photo of Kenneth Stanley

Kenneth Stanley

Uber AI Labs | University of Central Florida

Kenneth O. Stanley is the Charles Millican professor of computer science at the University of Central Florida where he’s director of the Evolutionary Complexity Research Group, and he’s a senior research science manager and head of Core AI research at Uber Labs. Previously, he was a cofounder of Geometric Intelligence, which was acquired by Uber to create Uber AI Labs. He’s an inventor of the neuroevolution of augmenting topologies (NEAT), HyperNEAT, and novelty search neuroevolution algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e., evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, and open-ended evolution. He’s won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, and Galactic Arms Race. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002–2012 from the International Society for Artificial Life. He’s a coauthor of the popular science book Why Greatness Cannot Be Planned: The Myth of the Objective (Springer) and has spoken widely on its subject. He earned a BSE from the University of Pennsylvania in 1997 and a PhD in 2004 from the University of Texas at Austin.