Accelerating Genetic Programming Using Graphics Processing Units

Tony Lewis
Birkbeck, University of London
University of London, 2012

   title={Accelerating Genetic Programming Using Graphics Processing Units},

   author={Lewis, Tony},



Download Download (PDF)   View View   Source Source   



Evolution through natural selection offers the possibility of automatically generating functionally complex solutions to a wide range of problems. Methods such as Genetic Programming (GP) show the promise of this approach but tend to stagnate after relatively few generations. To research this issue, execution speed must be substantially improved. This thesis presents work to accelerate the execution of such methods. The work uses the Graphics Processing Unit (GPU) to target the evaluation of individuals since this is the most time-consuming part of the run. Two models have been emerging for this: dynamically compiling each new generation of individuals for the GPU or using a single GPU interpreter, to which successive groups of individuals can be sent. Using the latter model, a GPU interpreter is constructed to implement cyclic GP, an advanced form of GP that imposes several challenging implementation issues which are addressed. Accelerating the evaluation using the GPU is only part of the story. The next part of the work interleaves CPU and GPU computation to keep both chips as busy as possible with the tasks to which they are best suited and then to recruit multiple GPUs and CPU cores to further accelerate the run. Using the former model, a compiling system is constructed and this is used to investigate two methods to overcome the primary difficulty with the approach: long compilation times. That system implements Tweaking Mutation Behaviour Learning (TMBL), a form focused on long term fitness growth and overcoming the previously mentioned stagnation issues. Further work optimises two CPU tasks highlighted by profiling: tournament selection and individual copying. These techniques are highly effective and permit much shorter run-times. This clears the way for research into stimulating long term fitness growth and hence for tackling new, complex problems.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1543 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

274 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: