TensorFlow Enabled Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Staats:2017:GECCO,

• author = "Kai Staats and Edward Pantridge and Marco Cavaglia and Iurii Milovanov and Arun Aniyan",
• title = "{TensorFlow} Enabled Genetic Programming",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "1872--1879",
• size = "8 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3084216",
• DOI = "doi:10.1145/3067695.3084216",
• acmid = "3084216",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, evolutionary computation, gpu, machine learning, multicore, parallel, tensorflow, vectorized",
• month = "15-19 " # jul,
• abstract = "Genetic Programming, a kind of evolutionary computation and machine learning algorithm, is shown to benefit significantly from the application of vectorized data and the TensorFlow numerical computation library on both CPU and GPU architectures. The open source, Python Karoo GP is employed for a series of 190 tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data points. This body of tests demonstrates that datasets measured in tens and hundreds of data points see 2--15x improvement when moving from the scalar/SymPy configuration to the vector/TensorFlow configuration, with a single core performing on par or better than multiple CPU cores and CPUs. A dataset composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing 5.5M data points sees GPU configurations out-performing CPU configurations on average by 1.3x.",
• notes = "Also known as \cite{Staats:2017:TEG:3067695.3084216} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Kai Staats Edward R Pantridge Marco Cavaglia Iurii Milovanov Arun Aniyan

Citations