Scalable GA processor architecture and its implementation of processor-element

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, premature convergence in early stage of evolution and proper adjustment of many GA parameters. In order to overcome the difficulties of GA, this paper describes the architecture of a scalable and high-speed GA processor, which is characterized by hardware-oriented approach based on Distributed GA, optimized hierarchic pipelines for high-speed evolutions and flexible genetic operations corresponding to a given problem. Furthermore, this paper also describes VLSI implementation of a processor-element to verify feasibility of our proposed architecture for applications.