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Utilizing Symmetry in Evolutionary Design
Active from 2007 - 2010
Can symmetry be utilized as a design principle to constrain evolutionary search, making it more effective? This dissertation aims to show that this is indeed the case, in two ways. First, an approach called ENSO is developed to evolve modular neural network controllers for simulated multilegged robots. Inspired by how symmetric organisms have evolved in nature, ENSO utilizes group theory to break symmetry systematically, constraining evolution to explore promising regions of the search space. As a result, it evolves effective controllers even when the appropriate symmetry constraints are difficult to design by hand. The controllers perform equally well when transferred from simulation to a physical robot. Second, the same principle is used to evolve minimal-size sorting networks. In this different domain, a different instantiation of the same principle is effective: building the desired symmetry step-by-step. This approach is more scalable than previous methods and finds smaller networks, thereby demonstrating that the principle is general. Thus, evolutionary search that utilizes symmetry constraints is shown to be effective in a range of challenging applications.
People
Vinod Valsalam
Ph.D. Alumni
vkv [at] alumni utexas net
Publications
Using Symmetry and Evolutionary Search to Minimize Sorting Networks
2013
Vinod K. Valsalam and Risto Miikkulainen,
Journal of Machine Learning Research
, Vol. 14, Feb (2013), pp. 303--331.
Constructing Controllers for Physical Multilegged Robots using the ENSO Neuroevolution Approach
2012
Vinod K. Valsalam, Jonathan Hiller, Robert MacCurdy, Hod Lipson and Risto Miikkulainen,
Evolutionary Intelligence
, Vol. 5, 1 (2012), pp. 1--12.
Evolving Symmetry for Modular System Design
2011
Vinod K. Valsalam and Risto Miikkulainen,
IEEE Transactions on Evolutionary Computation
, Vol. 15, 3 (2011), pp. 368--386.
Utilizing Symmetry and Evolutionary Search to Minimize Sorting Networks
2011
Vinod K. Valsalam and Risto Miikkulainen, Technical Report AITR-11-09, Department of Computer Sciences, The University of Texas at Austin.
Utilizing Symmetry in Evolutionary Design
2010
Vinod Valsalam, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI-10-04.
Evolving Symmetric and Modular Neural Network Controllers for Multilegged Robots
2009
Vinod K. Valsalam and Risto Miikkulainen, In
xploring New Horizons in Evolutionary Design of Robots: Workshop at the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2009.
Evolving Symmetric and Modular Neural Networks for Distributed Control
2009
Vinod K. Valsalam and Risto Miikkulainen, In
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
, pp. 731--738 2009.
Modular Neuroevolution for Multilegged Locomotion
2008
Vinod K. Valsalam and Risto Miikkulainen, In
Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008
, pp. 265-272, New York, NY, USA 2008. ACM.
Related Areas
Control
Evolutionary Computation
Neuroevolution
Robotics
Software/Data
ENSO
This package contains software implementing the ENSO approach for evolving symmetric modular neural networks. It also in...
2010
Sorting Networks
This package contains software utilizing an approach based on symmetry and evolution to minimize the number of comparato...
2010
Demos
Evolving Controller Symmetry for Multilegged Robots
Vinod Valsalam
2010
Evolving Controllers for Physical Multilegged Robots
Vinod Valsalam
2011
Modular Neuroevolution for Multilegged Locomotion
Vinod Valsalam
2008
Labs
Neural Networks