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."
Evolutionary Annealing: Global Optimization in Arbitrary Measure Spaces Alan J Lockett and Risto Miikkulainen Journal of Global Optimization, 58:75-108, 2014. 2014

A Measure-Theoretic Analysis of Stochastic Optimization Alan J. Lockett and Risto Miikkulainen In Proceedings of the 12th International Workshop on Foundations of Genetic Algorithms (FOGA-2013... 2013

Measure-Theoretic Analysis of Performance in Evolutionary Algorithms Alan J Lockett In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC-2013), 2013. IEEE P... 2013

Neuroannealing: Martingale-Driven Optimization for Neural Networks Alan J Lockett and Risto Miikkulainen In Proceedings of the 2013 Genetic and Evolutionary Computation Conference (GECCO-2013), 2013... 2013

General-Purpose Optimization Through Information-Maximization Alan J Lockett PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, 2012. Tech Report AI... 2012

Measure-Theoretic Evolutionary Annealing Alan J. Lockett and Risto Miikkulainen In Proceedings of the 2011 IEEE Congress on Evolutionary Computation, 2011. 2011

Real-Space Evolutionary Annealing Alan J Lockett and Risto Miikkulainen In Proceedings of the 2011 Genetic and Evolutionary Computation Conference (GECCO-2011), 2011... 2011

Temporal Convolution Machines for Sequence Learning Alan J Lockett and Risto Miikkulainen Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin, 2009. 2009

Evolving Opponent Models for Texas Hold 'Em Alan J Lockett and Risto Miikkulainen In IEEE Conference on Computational Intelligence in Games, Perth, Australia, 2008. 2008

Evolving Explicit Opponent Models for Game Play Alan Lockett, Charles Chen, and Risto Miikkulainen In Genetic and Evolutionary Computation Conference (GECCO-2007), 2007. 2007

PyEC Python package containing source code for Evolutionary Annealing along with a number of other evolutionary and stochasti... 2011