SwiftCMA
Released 2019
Download on GitHub

SwiftCMA is a pure-Swift implementation of Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES).

People
Santiago Gonzalez Ph.D. Alumni slgonzalez [at] utexas edu
Publications
Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization 2021
Santiago Gonzalez and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 305-313, 2021.
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization 2020
Santiago Gonzalez and Risto Miikkulainen, In Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, July 2020.
Areas of Interest
Evolutionary Computation
Labs
Neural Networks