DAE-GP: denoising autoencoder LSTM networks as probabilistic models in estimation of distribution genetic programming. - David Wittenberg; Franz Rothlauf; Dirk Schweim (GECCO, 2020)

Improving estimation of distribution genetic programming with novelty initialization. - Christian Olmscheid; David Wittenberg; Dominik Sobania; Franz Rothlauf (GECCO Companion, 2021)

On sampling error in evolutionary algorithms. - Dirk Schweim; David Wittenberg; Franz Rothlauf (GECCO Companion, 2021)

Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search. - David Wittenberg (EuroGP, 2022)

Analyzing optimized constants in genetic programming on a real-world regression problem. - Dominik Sobania; Martin Briesch; David Wittenberg; Franz Rothlauf (GECCO Companion, 2022)

Denoising autoencoder genetic programming for real-world symbolic regression. - David Wittenberg; Franz Rothlauf (GECCO Companion, 2022)

On sampling error in genetic programming. - Dirk Schweim; David Wittenberg; Franz Rothlauf (Nat. Comput., 2022)

Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming. - David Wittenberg; Franz Rothlauf (EuroGP, 2023)

Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude. - Johannes Reiter; Dirk Schweim; David Wittenberg (GECCO Companion, 2023)

Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search. - David Wittenberg; Franz Rothlauf; Christian Gagné 001 (Genet. Program. Evolvable Mach., 2023)