Genetic programming benchmarks

Author:

McDermott James1,Kronberger Gabriel2,Orzechowski Patryk3,Vanneschi Leonardo4,Manzoni Luca5,Kalkreuth Roman6,Castelli Mauro4

Affiliation:

1. University of Galway, Ireland

2. University of Applied Sciences Upper Austria

3. University of Pennsylvania and AGH University of Science and Technology, Krakow, Poland

4. Universidade Nova de Lisboa, Lisboa; Portugal

5. University of Trieste, Trieste, Italy

6. Leiden University, Netherlands

Abstract

The top image shows a set of scales, which are intended to bring to mind the ideas of balance and fair experimentation which are the focus of our article on genetic programming benchmarks in this issue. Image by Elena Mozhvilo and made available under the Unsplash license on https://unsplash.com/photos/j06gLuKK0GM.

Publisher

Association for Computing Machinery (ACM)

Subject

General Medicine

Reference38 articles.

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3. Burke , E. K. , Towards the decathlon challenge of search heuristics." Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference : Late Breaking Papers . 2009 . Burke, E. K., et al. "Towards the decathlon challenge of search heuristics." Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. 2009.

4. Burnham , K. P. Model selection and multimodel inference. A practical information-theoretic approach ( 1998 ). Burnham, K. P. Model selection and multimodel inference. A practical information-theoretic approach (1998).

5. Chen , X. , Liu , C. , & Song , D. ( 2018 , ). Execution-guided neural program synthesis . In International Conference on Learning Representations. ICLR Chen, X., Liu, C., & Song, D. (2018,). Execution-guided neural program synthesis. In International Conference on Learning Representations. ICLR

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