- author = "Darren M. Chitty",
- title = "A data parallel approach to genetic programming using programmable graphics hardware",
- booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation",
- year = "2007",
- editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener",
- volume = "2",
- isbn13 = "978-1-59593-697-4",
- pages = "1566--1573",
- address = "London",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1566.pdf",
-
DOI = "
doi:10.1145/1276958.1277274",
- publisher = "ACM Press",
- publisher_address = "New York, NY, USA",
- month = "7-11 " # jul,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, data parallelism, GPU, graphics cards, OpenGL, Cg",
- size = "8 pages",
- abstract = "In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community as low cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor architectures which can be achieved by harnessing the computing power of these next generation graphics cards.",
-
notes = "NVidia GeForce 6400 GO. Fisher Iris, x^4+x^3+x^2+x,
11-mux. Cg.
Cg toolkit used to compile each GP tree (fragment program) individually before transferring each separately to nVidia GeForce 6400 GO GPU. p1571 claims {"}changing the GPU program ... is relatively fast{"}. Iris, 11-Mux. Symbolic regression. Pop 500, 50 gens. Fig 11 10000 to 15000 fitness cases GPU big win over CPU.
sec 3.6 'only one [GP] can be ready to be executed at any one time'
GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007).
ACM Order Number 910071",
