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Parallel GPQUICK

Published: 13 July 2019 Publication History

Abstract

We modified GPQuick to use SIMD parallel floating point AVX 512 bit instructions and 48 threads to give up to 139 billion GP operations per second, 139 giga GPops, on a single Intel Xeon Gold 6126 2.60 GHz server. The multi-threaded single instruction multiple data genetic programming GP interpreter has evolved binary trees of more than 396 million instructions using subtree crossover and run populations for a million generations.

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References

[1]
M. J. Keith and M. C. Martin. Genetic programming in C++: Implementation issues. In K. E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 13, pages 285--310. MIT Press, 1994.
[2]
J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
[3]
J. R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts, May 1994.
[4]
W. B. Langdon. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!, volume 1 of Genetic Programming. Kluwer, Boston, 1998.
[5]
W. B. Langdon. Linear increase in tree height leads to sub-quadratic bloat. In T. Haynes et al., editors, Foundations of Genetic Programming, pages 55--56, Orlando, Florida, USA, 13 July 1999.
[6]
W. B. Langdon. Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines, 1(1/2):95--119, Apr. 2000.
[7]
W. B. Langdon. Large scale bioinformatics data mining with parallel genetic programming on graphics processing units. In S. Tsutsui and P. Collet, editors, Massively Parallel Evolutionary Computation on GPGPUs, Natural Computing Series, chapter 15, pages 311--347. Springer, 2013.
[8]
W. B. Langdon. Long-term evolution of genetic programming populations. In GECCO 2017: The Genetic and Evolutionary Computation Conference, pages 235--236, Berlin, 15--19 July 2017. ACM.
[9]
W. B. Langdon and W. Banzhaf. Faster genetic programming GPquick via multicore and advanced vector extensions. Technical Report RN/19/01, University College, London, London, UK, 23 Feb. 2019.
[10]
W. B. Langdon, T. Soule, R. Poli, and J. A. Foster. The evolution of size and shape. In L. Spector et al., editors, Advances in Genetic Programming 3, chapter 8, pages 163--190. MIT Press, Cambridge, MA, USA, June 1999.
[11]
R. Poli and W. B. Langdon. Sub-machine-code genetic programming. In L. Spector et al., editors, Advances in Genetic Programming 3, chapter 13, pages 301--323. MIT Press, Cambridge, MA, USA, June 1999.
[12]
R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008. (With contributions by J. R. Koza).
[13]
R. Poli and J. Page. Solving high-order Boolean parity problems with smooth uniform crossover, sub-machine code GP and demes. Genetic Programming and Evolvable Machines, 1(1/2):37--56, Apr. 2000.
[14]
R. Sedgewick and P. Flajolet. An Introduction to the Analysis of Algorithms. Addison-Wesley, 1996.
[15]
A. Singleton. Genetic programming with C++. BYTE, pages 171--176, Feb. 1994.
[16]
G. Syswerda. A study of reproduction in generational and steady state genetic algorithms. In G. J. E. Rawlings, editor, Foundations of genetic algorithms, pages 94--101. Morgan Kaufmann, Indiana University, 15--18 July 1990. Published 1991.

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  • (2024)Sustaining Evolution for Shallow Embodied IntelligenceIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/1321/1/0120071321:1(012007)Online publication date: 1-Dec-2024
  • (2022)Long-Term Evolution Experiment with Genetic ProgrammingArtificial Life10.1162/artl_a_0036028:2(173-204)Online publication date: 28-Jun-2022
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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 13 July 2019

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Author Tags

  1. evolutionary computing
  2. performance
  3. sextic polynomial

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2025)Using FPGA devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysisGenetic Programming and Evolvable Machines10.1007/s10710-024-09505-226:1Online publication date: 7-Jan-2025
  • (2024)Sustaining Evolution for Shallow Embodied IntelligenceIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/1321/1/0120071321:1(012007)Online publication date: 1-Dec-2024
  • (2022)Long-Term Evolution Experiment with Genetic ProgrammingArtificial Life10.1162/artl_a_0036028:2(173-204)Online publication date: 28-Jun-2022
  • (2022)Evolving open complexityACM SIGEVOlution10.1145/3532942.353294515:1(1-4)Online publication date: 20-Apr-2022
  • (2022)Genetic programming convergenceProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534063(27-28)Online publication date: 9-Jul-2022
  • (2022)Fitness FirstGenetic Programming Theory and Practice XVIII10.1007/978-981-16-8113-4_8(143-164)Online publication date: 11-Feb-2022
  • (2021)Genetic programming convergenceGenetic Programming and Evolvable Machines10.1007/s10710-021-09405-923:1(71-104)Online publication date: 30-Aug-2021
  • (2021)Incremental Evaluation in Genetic ProgrammingGenetic Programming10.1007/978-3-030-72812-0_15(229-246)Online publication date: 25-Mar-2021
  • (2020)Genetic Improvement of Genetic Programming2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185771(1-8)Online publication date: Jul-2020

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