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
Evolutionary Annealing: Global Optimization in Arbitrary Measure Spaces 2013
Alan J Lockett and Risto Miikkulainen, Journal of Global Optimization (2013), pp. 1--34.
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