GPUs for Genetic and Evolutionary Computation
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The Goal This competition focuses on the applications of genetic and evolutionary computation that can maximally exploit the parallelism provided by low-cost consumer graphical cards. The competition will award the best applications both in terms of degree of parallelism obtained, in terms of overall speed-up, and in terms of programming style. The Competition We had 10 excellent submissions that have now been reviewed by our panel. At the CIGPU workshop and during the competition slot, the best 3 entrants will be given time to present their work. During the competition session, ballot papers will be distributed for selecting the overall winner. |
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The Submissions
Winning entry
Nicholas A. Sinnott-Armstrong, Casey S. Greene and Jason H. Moore - Using Evolutionary Computing on Consumer Graphics Hardware for Epistasis Analysis in Human Genetics
Runners up
Luca Mussi and Stefano Cagnoni - Particle Swarm Optimization within the CUDA Architecture
Steve Worley - Optimization of Primality Testing Methods by GPU Evolutionary Search
Other entries
Avi Dullu - A Framework for Genetic Algorithms for GPGPUs
Stefano Debattisti, Nicola Marlat, Luca Mussi and Stefano Cagnoni - Implementation of a Simple Genetic Algorithm within the CUDA Architecture
Ying-Shiuan You - Parallel Ant System for Traveling Salesman Problem on GPUs
William Langdon - A CUDA SIMT Interpreter for Genetic Programming
Petr Pospichal and Jiri Jaros - The GPU-based Acceleration of the Genetic Algorithm
Marc Ebner - A GPU Accelerated Evolutionary Computer Vision System
Noriyuki Fujimoto and Shigeyoshi Tsutsui - A QAP Solver with CUDA GPU Computing Architecture
On behalf of the organisers, I would like to offer a big thank you to all those who entered. (Simon Harding)