Migration strategy and mathematical analysis of sub-population size adaptation in parallel genetic algorithm
Xue-jing Gong, Jian-wei Xie · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Adaptive Parallel Genetic Algorithm adjusts the genetic parameters and operators dynamically during the iterations of evolution in order to accelerate the convergence and avoid the premature. By using the concept of coarse-grained parallelization, the population is divided into a few large subpopulations. These subpopulations evolve independently and concurrently on different processors. After a predefined period of time, some selected individuals are exchanged via a migration process. In this paper, a parallel multi-population adaptive genetic algorithm is proposed by adjusting the size of sub-population. The sub-population size is dynamically varied based on the fitness of the best individual of that sub-population compared with the mean fitness of the total population. The relevant migration strategy including synchronous and asynchronous migration is also put forward to avoid the work load imbalance in parallel genetic algorithm. Then, the convergence analysis based on schema theory is given to certify the efficiency of the Sub- Populations size adjustment in the algorithm.