Analysis of the numerical effects of parallelism on a parallel genetic algorithm

Examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments provide preliminary evidence that asynchronous versions of these algorithms have a lower run-time than synchronous GAs. Our analysis shows that this improvement is due to (1) reduced synchronization costs and (2) higher numerical efficiency (e.g. fewer function evaluations) for the asynchronous GAs. This analysis includes a critique of the utility of traditional parallel performance measures for parallel GAs.