Performance investigations of genetic algorithms on graphics cards

Abstract

Genetic algorithms are one of the most adaptable optimization algorithms. Due to their inherent parallelism they seem well suited for the execution on massively parallel hardware such as graphics processing units. In this paper we put this claim to the test by performing comprehensive experiments. We try to find out how well graphics processing units are suited for the task and what parts of genetic algorithms should be executed on them. We focus especially on the new Fermi generation of Nvidia graphics chips. While it is imperative the fitness function be effectively parallelizable on the GPU, because it is the most computational expensive task of the algorithm, results indicate that if this is the case, speedups of several orders of magnitude are possible compared to conventional multi-core CPUs. Our findings also suggest that, starting with the Fermi architecture, all parts of a genetic algorithm should be carried out on the graphics card instead of only part of it.