Theory of Evolutionary Computation
Our work focuses on applying measure theory and martingale analysis to develop new evolutionary algorithms with known properties, as well as a theoretical characterization, performance measures, and convergence and no-free-lunch analysis of evolutionary computation methods in general.
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

Evolutionary Annealing: Global Optimization in Arbitrary Measure Spaces Alan J Lockett and Risto Miikkulainen Journal of Global Optimization:1--34, April 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

Alan J. Lockett Ph.D. Alumni alan lockett [at] gmail com
Risto Miikkulainen Faculty risto [at] cs utexas edu
PyEC Python package containing source code for Evolutionary Annealing along with a number of other evolutionary and stochasti... 2011