Implementation of Genetic Algorithm Based on Hardware Optimization

초록

Genetic algorithm(GA) has been known as a method of solving large-scaled optimization problems with complex constrains in various applications. Since a major drawback of the GA is that it needs a long computation time, the hardware implementations of GA processors(GAP) are focused on in recent studies. In this paper we proposed a more efficient GAP based on steady-state GA, modified survival-based GA, and modified tourament selection. In addition, by employing the efficient pipeline parallelization and handshaking protocol in our GAP, almost 50% of the computation speed-up can be achieved over survival-based GA, which runs one million crossovers per second(1MHz).