Morphology, Evolution and Cognition Laboratory
Department of Computer Science
University of Vermont
Investigations at the Interface of Morphology, Evolution and Cognition
Monday, September 13, 2010
Lecture Hall 2, 5:00 PM
In this talk I will discuss a project in which I used a genetic algorithm to evolve simulated genetic regulatory networks. The GRNs are used to grow robots in a virtual environment, and the robots are then selected for their ability to perform a desired task. I will show how the GRNs were shaped by selection in this simple system to favor particular kinds of robot morphologies, neural circuits, and GRN architectures. I will argue that this approach can be used to investigate what Tinbergen called the ultimate mechanisms of behavior: i.e. understanding what environments and evolutionary pressures favored the development of particular morphological and neurological structures to realize a useful behavior.
Josh Bongard received his Bachelors degree in Computer Science from McMaster University, Canada, his Masters degree from the University of Sussex, UK, and his PhD from the University of Zurich, Switzerland. He served as a postdoctoral associate under Hod Lipson in the Computational Synthesis Laboratory at Cornell University from 2003 to 2006. He is the co-author of the popular science book entitled “How the Body Shapes the Way We Think: A New View of Intelligence,” MIT Press, November 2006 (with Rolf Pfeifer). Currently, he is an assistant professor in the Computer Science Department at the University of Vermont. His research interests include embodied cognition and evolutionary computation, and he was named both a Microsoft Research New Faculty Fellow in 2006, as well as a member of the TR35: MIT Technology Review’s top 35 innovators under the age of 35.
- Bongard, J. C. “Evolving modular genetic regulatory networks”, in Proceedings of the IEEE 2002 Congress on Evolutionary Computation, IEEE Press, pp. 1872-1877, 2002.
- Bongard, J., Zykov, V. and Lipson, H. “Resilient machines through continuous self-modeling”, Science, 314: 1118-1121, 2007. (related Perspectives article/PDF) Be sure to click here to check out more pictures and videos of the robots described in this paper!