A FPGA implementation of a self-adaptive genetic algorithm
Nonel S. Thirer · 2020
A genetic algorithm (GA) is an iterative procedure which performs several processes with the population individuals (chromosomes) to produce a new population, like in the biological evolution. To avoid the premature convergence, the paper proposes a self-adaptive algorithm, which adjusts parameters at the chromosome level and also at the population level, to solve a gender-based GA. Because the FPGA implementation of a self-adaptive GA requires more complicated logic units as for a conventional GA implementation, we propose to optimize this implementation by using a soft or hard processor embedded in the FPGA chip. Thus a part of the tasks will be solved by hardware blocks and a part of the tasks will be solved by the processor.