A general-purpose framework for FPGA-accelerated genetic algorithms Online publication date: Thu, 26-Nov-2015
by Liucheng Guo; Andreea Ingrid Funie; Zhongliu Xie; David Thomas; Wayne Luk
International Journal of Bio-Inspired Computation (IJBIC), Vol. 7, No. 6, 2015
Abstract: 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 study proposes a general-purpose automated framework for creating and executing a GA system on FPGAs. This framework contains scalable and customisable hardware architectures while providing a unified platform for different chromosomes. At compile-time, only a high-level 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 finding further good configurations of GA execution. The framework was tested on a high performance FPGA platform using nine problems and benchmarks, including the travelling salesman problem, a locating problem and the NP-hard set covering problem. Experiments show the system's flexibility and an average speedup of 29 times over a multi-core CPU.
Online publication date: Thu, 26-Nov-2015
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bio-Inspired Computation (IJBIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.If you still need assistance, please email firstname.lastname@example.org