Backend-agnostic Tree Evaluation for Genetic Programming

Author:

Burlacu Bogdan1ORCID

Affiliation:

1. University of Applied Sciences Upper Austria, Linz, Austria

Publisher

ACM

Reference31 articles.

1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.

2. Energy Efficiency Effects of Vectorization in Data Reuse Transformations for Many-Core Processors—A Case Study †

3. Francisco Baeta, João Correia, Tiago Martins, and Penousal Machado. 2021. TensorGP - Genetic Programming Engine in TensorFlow. In Applications of Evolutionary Computation, Pedro A. Castillo and Juan Luis Jiménez Laredo (Eds.). Springer International Publishing, Cham, 763--778.

4. Operon C++

5. William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, and Jason H. Moore. 2021. Contemporary Symbolic Regression Methods and their Relative Performance. arXiv:2107.14351 [cs.NE]

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