Paper Info
Title
HGA: a hardware-based genetic algorithm
Abstract
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.
Year
DOI
Venue
1995
10.1145/201310.201319
FPGA '95 Proceedings of the 1995 ACM third international symposium on Field-programmable gate arrays
Keywords
DocType
ISBN
hardware-based ga,software routine,performance acceleration,genetic algorithm,prototype hga,similar ga,function optimization,certain ga module,software-based ga,general-purpose ga engine,functional hardware-based genetic algorithm,ga parameter,parallel genetic algorithms,performance evaluation,field programmable gate arrays,proof of concept,natural selection,field programmable gate array
Conference
0-89791-743-X
Citations 
PageRank 
References 
72
8.12
6
Authors
3
Name
Order
Citations
PageRank
Stephen Scott140733.22
A Samal21033213.54
Sharad C. Seth367193.61