Article

Evolution of vertex and pixel shaders

Authors:

Markus Reinhardt,

Jürgen AlbertAuthors Info & Claims

EuroGP'05: Proceedings of the 8th European conference on Genetic Programming

Pages 261 - 270

https://doi.org/10.1007/978-3-540-31989-4_23

Published: 30 March 2005 Publication History

Abstract

In real-time rendering, objects are represented using polygons or triangles. Triangles are easy to render and graphics hardware is highly optimized for rendering of triangles. Initially, the shading computations were carried out by dedicated hardwired algorithms for each vertex and then interpolated by the rasterizer. Todays graphics hardware contains vertex and pixel shaders which can be reprogrammed by the user. Vertex and pixel shaders allow almost arbitrary computations per vertex respectively per pixel. We have developed a system to evolve such programs. The system runs on a variety of graphics hardware due to the use of NVIDIA's high level Cg shader language. Fitness of the shaders is determined by user interaction. Both fixed length and variable length genomes are supported. The system is highly customizable. Each individual consists of a series of meta commands. The resulting Cg program is translated into the low level commands which are required for the particular graphics hardware.

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Cited By

Lanzi PLoiacono DSilva SPaquete L(2023)ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game DesignProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590351(1383-1390)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590351
Ibrahim A(2017)Multiple-process procedural textureThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-016-1295-z33:12(1511-1528)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s00371-016-1295-z
Lewis TMagoulas GLanzi P(2011)TMBL kernels for CUDA GPUs compile faster using PTXProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002033(455-462)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2002033
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Information & Contributors

Information

Published In

cover image Guide Proceedings

EuroGP'05: Proceedings of the 8th European conference on Genetic Programming

March 2005

380 pages

ISBN:3540254366

Editors:
Maarten Keijzer
No Affiliation
,
Andrea Tettamanzi
Dipartimento di Tecnologie dell'Informazione, Università di Milano Crema
,
Pierre Collet
Dipartimento di Tecnologie dell'Informazione, Université Louis Pasteur, LSIIT, FDBT, Pôle API, Illkirch, France
,
Jano Hemert
National e-Science Centre, University of Edinburgh, Illkirch, United Kingdom
,
Marco Tomassini
Institut des Systèmes d'Information (ISI), Université de Lausanne, Hautes Etudes Commerciales (HEC), Illkirch

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 30 March 2005

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Cited By

Lanzi PLoiacono DSilva SPaquete L(2023)ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game DesignProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590351(1383-1390)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590351
Ibrahim A(2017)Multiple-process procedural textureThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-016-1295-z33:12(1511-1528)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s00371-016-1295-z
Lewis TMagoulas GLanzi P(2011)TMBL kernels for CUDA GPUs compile faster using PTXProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002033(455-462)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2002033
Wilson GBanzhaf W(2010)Deployment of parallel linear genetic programming using GPUs on PC and video game console platformsGenetic Programming and Evolvable Machines10.1007/s10710-010-9102-511:2(147-184)Online publication date: 1-Jun-2010
https://dl.acm.org/doi/10.1007/s10710-010-9102-5
Langdon W(2010)A many threaded CUDA interpreter for genetic programmingProceedings of the 13th European conference on Genetic Programming10.1007/978-3-642-12148-7_13(146-158)Online publication date: 7-Apr-2010
https://dl.acm.org/doi/10.1007/978-3-642-12148-7_13
Langdon WBanzhaf W(2008)A SIMD interpreter for genetic programming on GPU graphics cardsProceedings of the 11th European conference on Genetic programming10.5555/1792694.1792702(73-85)Online publication date: 26-Mar-2008
https://dl.acm.org/doi/10.5555/1792694.1792702
Harding SBanzhaf W(2007)Fast genetic programming on GPUsProceedings of the 10th European conference on Genetic programming10.5555/1763756.1763766(90-101)Online publication date: 11-Apr-2007
https://dl.acm.org/doi/10.5555/1763756.1763766
Collomosse J(2006)Supervised genetic search for parameter selection in painterly renderingProceedings of the 2006 international conference on Applications of Evolutionary Computing10.1007/11732242_57(599-610)Online publication date: 10-Apr-2006
https://dl.acm.org/doi/10.1007/11732242_57

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