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.
Alan J. Lockett Ph.D. Alumni alan lockett [at] gmail com
Risto Miikkulainen Faculty risto [at] cs utexas edu
Discovering Evolutionary Stepping Stones through Behavior Domination 2017
Elliot Meyerson and Risto Miikkulainen, To Appear In Proceedings of The Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, July 2017. ACM. nominated for Best Paper Award.
Estimating the Advantage of Age-Layering in Evolutionary Algorithms 2016
Hormoz Shahrzad, Babak Hodjat, and Risto Miikkulainen, To Appear In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2016, Denver, CO) 2016.
Evolutionary Annealing: Global Optimization in Arbitrary Measure Spaces 2014
Alan J Lockett and Risto Miikkulainen, Journal of Global Optimization, Vol. 58 (2014), pp. 75-108.
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.
PyEC Python package containing source code for Evolutionary Annealing along with a number of other evolutionary and stochasti... 2011