HGA: a hardware-based genetic algorithm

Stephen D. Scott, Ashok Samal, Sharad Seth

Research output: Contribution to conferencePaperpeer-review

122 Scopus citations


A genetic algorithm (GA) is a robust problem-solving method based on natural selection. Hardware's speed advantage and its ability to parallelize offer great rewards to genetic algorithms Speedups of 1-3 orders of magnitude have been observed when frequently used software routines were implemented in hardware by way of reprogrammable field-programmable gate arrays (FPGAs). Reprogrammability is essential in a general-purpose GA engine because certain GA modules require changeability (e.g. the function to be optimized by the GA). Thus a hardware-based GA is both feasible and desirable. A fully functional hardware-based genetic algorithm (the HGA) is presented here as a proof-of-concept system. It was designed using VHDL to allow for easy scalability. It is designed to act as a coprocessor with the CPU of a PC. The user programs the FPGAs which implement the function to be optimized. Other GA parameters may also be specified by the user. Simulation results and performance analyses of the HGA are presented. A prototype HGA is described and compared to a similar GA implemented in software. In the simple tests, the prototype took about 6% as many clock cycles to run as the software-based GA. Further suggested improvements could realistically make the HGA 2-3 orders of magnitude faster than the software-based GA.

Original languageEnglish (US)
Number of pages7
StatePublished - 1995
Externally publishedYes
EventProceedings of the 1995 ACM 3rd International Symposium on Field-Programmable Gate Arrays - Monterey, CA, USA
Duration: Feb 12 1995Feb 14 1995


OtherProceedings of the 1995 ACM 3rd International Symposium on Field-Programmable Gate Arrays
CityMonterey, CA, USA

ASJC Scopus subject areas

  • Computer Science(all)


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