Techniques to Improve Exploration Efficiency of Parallel Self Adaptive Genetic Algorithms by Dispensing Synchronization

Exploration efficiency of GAs depends on parameter values such as mutation rate and crossover rate. To save labor of manually adjusting these values, GAs which automatically adjust parameter values(Adaptive GAs) have been proposed. We have proposed Self Adaptive Island GA(SAIGA), which does not require adjusting parameter values, and has search performance comparable to that of SGA with manually optimized parameter values. There are also parallel adaptive GAs proposed, but existing parallel adaptive GAs require periodical synchronization, and thus performance degrades if there are differences in performance between computers executed in parallel. In this paper, we propose Asynchronous SAIGA which does not require synchronization. We confirmed that it has same or more search performance than SAIGA