Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude
Author:
Affiliation:
1. University of Applied Sciences Mainz, Mainz, Germany
2. Johannes Gutenberg University, Mainz, Germany
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3583133.3596332
Reference10 articles.
1. François Chollet. 2015. keras. https://github.com/fchollet/keras. François Chollet. 2015. keras. https://github.com/fchollet/keras.
2. Improving estimation of distribution genetic programming with novelty initialization
3. On sampling error in evolutionary algorithms
4. On sampling error in genetic programming;Schweim Dirk;Natural Computing,2021
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A Functional Analysis Approach to Symbolic Regression;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14
2. Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search;Genetic Programming and Evolvable Machines;2023-11-08
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