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.
Discovering Evolutionary Stepping Stones through Behavior Domination Elliot Meyerson and Risto Miikkulainen To Appear In Proceedings of The Genetic and Evolutionary Computation Conference (GECCO 2017),... 2017

Estimating the Advantage of Age-Layering in Evolutionary Algorithms Hormoz Shahrzad, Babak Hodjat, and Risto Miikkulainen To Appear In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2016, Denv... 2016

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

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

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

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