Technical University of Cluj-Napoca
One very promising approach for solving complex optimizing and search problems is the Genetic Algorithm (GA) one. Based on this scheme a population of abstract representations of candidate solutions to an optimization problem gradually evolves toward better solutions. The aim is the optimization of a given function, the so called fitness function, which is evaluated upon the initial population as well as upon the solutions after successive generations. In this paper, the authors present the design of a GA and its implementation on state-of-the-art FPGAs. Their approach optimizes significantly more fitness functions than any other proposed solution. Several experiments on a platform with a Virtex-II Pro FPGA have been conducted.