Numerical and real time analysis of parallel distributed GAs with structured and panmictic populations

Parallel genetic algorithms (PGAs) have been traditionally used to overcome the intense use of CPU and memory that serial GAs need to solve complex problems. Non-parallel GAs can be classified into two classes: panmictic and structured-population algorithms. The difference relies on whether any individual in the population can mate with any other one or not. In this work they both are considered as two reproductive loop types executed in the islands of a parallel distributed GA. Our aim is to extend the existing studies on more conventional sequential islands to other kinds of evolution. A key issue in such a distributed PGA is the migration policy. The paper investigates the influence of the migration frequency and the migrant selection in a ring of islands performing either steady-state or cellular GAs. The study uses different problem types, namely deceptive, multimodal, NP-complete, and epistatic search landscapes, in order to provide a wide spectrum of problem difficulty to sustain the results. Large isolation values and random selection of the migrants are shown to provide a better success rate and a lower number of visited points. Also, some differences are pointed out in the behavior of panmictic and structured populations. Finally, the results show the advantages of an asynchronous migration step in the distributed GA