A pipeline-based genetic algorithm accelerator for time-critical processes in real-time systems

The meta-heuristic methods, genetic algorithms (GAs), are frequently used to obtain optimal solutions for some complicated problems. However, due to the characteristic of natural evolution, the methods slowly converge the derived solutions to an optimal solution and are usually used to solve complicated and offline problems. While, in a real-world scenario, there are some complicated but real-time problems that require being solved within a short response time and have to obtain an optimal or near optimal solution due to performance considerations. Thus, the convergence speed of GAs becomes an important issue when it is applied to solve time-critical optimization problems. To address this, this paper presents a novel method, named hyper-generation GA (HG-GA), to improve the convergence speed of GAs. The proposed HG-GA breaks the general rule of generation-based evolution and uses a pipeline operation to accelerate the convergence speed of obtaining an optimal solution. Based on an example of a time-critical scheduling process in an optical network, both analysis and simulation results show that the HG-GA can generate more and better chromosomes than general GAs within the same evolutionary period. The rapid convergence property of the HG-GA increases its potential to solve many complicated problems in real-time systems.