EXA-GP: Unifying Graph-Based Genetic Programming and Neuroevolution for Explainable Time Series Forecasting
Author:
Affiliation:
1. Rochester Institute of Technology, Rochester, New York, United States of America
Funder
National Sicence Foundation
Federal Aviation Administration
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3638530.3654349
Reference23 articles.
1. Evolving Graphs by Graph Programming
2. Neuro-Evolutionary Transfer Learning Through Structural Adaptation
3. Improving neuroevolutionary transfer learning of deep recurrent neural networks through network-aware adaptation
4. What's inside the black-box?
5. Leonardo Augusto Ferreira, Frederico Gadelha Guimarães, and Rodrigo Silva. 2020. Applying genetic programming to improve interpretability in machine learning models. In 2020 IEEE congress on evolutionary computation (CEC). IEEE, 1--8.
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