Architecture for High Speed Evolutionary Computation

Genetic Algorithm(GA) is widely known as a general-purpose optimization method, which can provide sub-optimum solutions for various optimization problems by means of modeling genetic evolutionary process of creatures. Several essential difficulties exist in GA, however, with regard to large amount of computation time and proper adjustment of many GA parameters. In order to overcome the difficulties of GA, this paper describes the architecture for a high speed evolutionary computation, which is optimized hierarchic pipelines to take generation models into consideration and can be flexible genetic operations corresponding to a given problems. Simulation results evaluating the proposed architecture show to achieve 193 times speed on average compared with software processing.