Parallel Genetic Algorithms on Multiple FPGAs

Abstract

Genetic algorithms (GA) have been shown to be effective in the optimization of many large-scale real-world problems in a reasonable amount of time. Parallel GAs not only reduce the overall GA execution time, but also bring higher quality solutions due to parallel search in multiple parts of the solution space. This paper proposes a parallel GA system on hardware such as Field-Programmable-Gate-Arrays (FPGAs). Our approach targets multiple FPGAs by exploring different search areas of the same solution space with different behaviours. Each FPGA contains an optimised customisable GA which can be configured using run-time parameters, removing the need for expensive recompilation. This paper also explores adjustment of the migration gap, providing empirical guidance on good settings to users. Experiments on three problems show the high performance of our system, with a 30 times speedup achieved compared to a multi-core CPU-based implementation.