DAE-GP
Author:
Affiliation:
1. Johannes Gutenberg-Universität, Mainz, Germany
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3377930.3390180
Reference35 articles.
1. Exploration and exploitation in evolutionary algorithms
2. What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes
3. Learning to Forget: Continual Prediction with LSTM
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1. Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search;Genetic Programming and Evolvable Machines;2023-11-08
2. Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude;Proceedings of the Companion Conference on Genetic and Evolutionary Computation;2023-07-15
3. Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression?;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12
4. Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming;Lecture Notes in Computer Science;2023
5. A hierarchical estimation of multi-modal distribution programming for regression problems;Knowledge-Based Systems;2023-01
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