GPUs for Genetic and Evolutionary Computation
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.
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.
Nicholas A. Sinnott-Armstrong, Casey S. Greene and Jason H. Moore - Using Evolutionary Computing on Consumer Graphics Hardware for Epistasis Analysis in Human Genetics
Luca Mussi and Stefano Cagnoni - Particle Swarm Optimization within the CUDA Architecture
Steve Worley - Optimization of Primality Testing Methods by GPU Evolutionary Search
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)