Narrow parallel genetic algorithm based on shared storage

The genetic algorithm is simple, easy to calculate and easy distribution of parallel processing, etc., based on this advantage, genetic algorithms are widely used in many fields within the range, such as machine learning, industrial control. In order to solve difficult nonlinear and related issues, narrow parallel genetic algorithm based on shared storage, you can effectively achieve data-level parallelism, with a strong degree of parallelism, which requires less communication overhead can be obtained higher efficiency than the original, at least to more than 50% increase. This paper elaborates on shared memory parallel narrow genetic algorithms, simulation results verify the correctness and efficiency.