  Genetic Algorithms (GAs) are used to solve search and optimization problems
in which an optimal solution can be found using an iterative process with
probabilistic and non-deterministic transitions. However, depending on the
problem's nature, the time required to find a solution can be high in
sequential machines due to the computational complexity of genetic algorithms.
This work proposes a parallel implementation of a genetic algorithm on
field-programmable gate array (FPGA). Optimization of the system's processing
time is the main goal of this project. Results associated with the processing
time and area occupancy (on FPGA) for various population sizes are analyzed.
Studies concerning the accuracy of the GA response for the optimization of two
variables functions were also evaluated for the hardware implementation.
However, the high-performance implementation proposes in this paper is able to
work with more variable from some adjustments on hardware architecture.
