Alan J. Lockett
Ph.D. Alumni
Alan's work with NNRG focused on
1. Evolutionary Annealing -- a martingale-based approach to optimization.
2. Neuroannealing -- using evolutionary annealing to learn neural networks.
3. Formal analysis of Iterative Stochastic Optimizers -- a measure theoretic approach to analyzing optimizer performance.

After graduating, he moved to a postdoc position with Prof. Juergen Schmidhuber at IDSIA in Lugano, Switzerland beginning. His position is funded by an NSF grant under the International Research Fellows Program, and the topic is "Deep Neural Networks for the Integration of Perception and Action in Robotic Controllers."
A Measure-Theoretic Analysis of Stochastic Optimization 2013
Alan J. Lockett and Risto Miikkulainen, In Proceedings of the 12th International Workshop on Foundations of Genetic Algorithms (FOGA-2013) 2013. ACM Press.
Evolutionary Annealing: Global Optimization in Arbitrary Measure Spaces 2013
Alan J Lockett and Risto Miikkulainen, Journal of Global Optimization (2013), pp. 1--34.
Measure-Theoretic Analysis of Performance in Evolutionary Algorithms 2013
Alan J Lockett, In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC-2013) 2013. IEEE Press.
Neuroannealing: Martingale-Driven Optimization for Neural Networks 2013
Alan J Lockett and Risto Miikkulainen, In Proceedings of the 2013 Genetic and Evolutionary Computation Conference (GECCO-2013) 2013. ACM Press.
General-Purpose Optimization Through Information-Maximization 2012
Alan J Lockett, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Tech Report AI12-11.
Measure-Theoretic Evolutionary Annealing 2011
Alan J. Lockett and Risto Miikkulainen, In Proceedings of the 2011 IEEE Congress on Evolutionary Computation 2011.
Real-Space Evolutionary Annealing 2011
Alan J Lockett and Risto Miikkulainen, In Proceedings of the 2011 Genetic and Evolutionary Computation Conference (GECCO-2011) 2011.
Temporal Convolution Machines for Sequence Learning 2009
Alan J Lockett and Risto Miikkulainen, Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin.
Evolving Opponent Models for Texas Hold 'Em 2008
Alan J Lockett and Risto Miikkulainen, In IEEE Conference on Computational Intelligence in Games, Perth, Australia 2008.
Evolving Explicit Opponent Models for Game Play 2007
Alan Lockett, Charles Chen, and Risto Miikkulainen, In Genetic and Evolutionary Computation Conference (GECCO-2007) 2007.
PyEC Python package containing source code for Evolutionary Annealing along with a number of other evolutionary and stochasti... 2011

Formerly affiliated with Neural Networks