The implementation of GA processor with multiple operators, based on subpopulation architecture

We propose the hardware-oriented genetic algorithm processor with efficient exploration based on a subpopulation architecture for high-performance convergence and reducing computation time. We applied the steady-state model among continuous generation model, modified tournament selection, special survival condition and the parallelism of coarse-grain to our proposed GAP. In addition, the crossover operator selection method with respect to the convergence state of each subpopulation was newly employed. In order to implement the efficient hardware structure, the pipelined structure was used. The proposed GAP is implemented on the AGENT2000 board with EFP10K200SRC device.