GECCO'2022 Symbolic Regression Competition: Post-Analysis of the Operon Framework

Author:

Burlacu Bogdan1ORCID

Affiliation:

1. Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Upper Austria, Austria

Publisher

ACM

Reference36 articles.

1. A Survey of Statistical Machine Learning Elements in Genetic Programming

2. Optuna

3. Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set

4. Deaglan J. Bartlett , Harry Desmond , and Pedro G . Ferreira . 2022 . Exhaustive Symbolic Regression . arXiv:2211.11461 [astro-ph.CO] Deaglan J. Bartlett, Harry Desmond, and Pedro G. Ferreira. 2022. Exhaustive Symbolic Regression. arXiv:2211.11461 [astro-ph.CO]

5. James Bergstra , Rémi Bardenet , Yoshua Bengio , and Balázs Kégl . 2011 . Algorithms for Hyper-Parameter Optimization. In Advances in Neural Information Processing Systems, J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K.Q . Weinberger (Eds.) , Vol. 24 . Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/ 2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011. Algorithms for Hyper-Parameter Optimization. In Advances in Neural Information Processing Systems, J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K.Q. Weinberger (Eds.), Vol. 24. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf

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