On the Accelerated Convergence of Genetic Algorithm Using GPU Parallel Operations

The genetic algorithm plays a very important role in many areas of applications. In this research, the authors propose to accelerate the evolution speed of the genetic algorithm by parallel computing, and optimize parallel genetic algorithms by methods such as the island model. The authors find that when the amount of population increases, the genetic algorithm tends to converge more rapidly into the global optimal solution; however, it also consumes greater amount of computation resources. To solve this problem, the authors take advantage of the many cores of GPUs to enhance computation efficiency and develop a parallel genetic algorithm for GPUs. Different from the usual genetic algorithm that uses one thread for computation of each chromosome, the parallel genetic algorithm using GPUs evokes large amount of threads simultaneously and allows the population to scale greatly. The large amount of the next generation population of chromosomes can be divided by a block method; and after independently operating in each block for a few generation, selection and crossover operations of chromosomes can be performed among blocks to greatly accelerate the speed to find the global optimal solution. Also, the travelling salesman problem TSP is used as the benchmark for performance comparison of the GPU and CPU; however, the authors did not perform algebraic optimization for TSP.