An Automated Framework for General-Purpose Genetic Algorithms on FPGAs

FPGA-based Genetic Algorithms (GAs) can effectively optimise complex applications, but require extensive hardware architecture customisation. To promote these accelerated GAs to potential users without hardware design experience, this paper proposes an automated framework for creating and executing a general-purpose GA system on FPGAs. This framework is a scalable and customisable hardware architecture while providing a unified platform for different chromosomes. At compile-time, only a highlevel input of the target application needs to be provided, without any hardware-specific code being necessary. At run-time, application inputs and GA parameters can be tuned, without time-consuming recompilation, for further good GA executions configuration to be found. The framework was tested on a high performance FPGA platform using six problems and benchmarks, including the Travelling Salesman problem, a locating problem and the NP-hard set covering problem. Experiments show the system’s flexibility as well as an average speed-up of 29 times compared to a multi-core CPU.