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.
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A Biological Perspective on Evolutionary Computation Risto Miikkulainen and Stephanie Forrest Nature Machine Intelligence, 3:9-15, 2021. 2021

Effective Regularization Through Loss-Function Metalearning Santiago Gonzalez and Risto Miikkulainen In arXiv:2010.00788, 2021. 2021

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

A Probabilistic Re-Formulation of No Free Lunch: Continuous Lunches Are Not Free Alan J. Lockett and Risto Miikkulainen Evolutionary Computation, 25:503--528, 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

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

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

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