Grafted Genetic Algorithm and Its Application

摘要

Genetic algorithm has found its appliction in various engineering areas, but it has the limitation of premature convergence and exhausting computing amount when applied in the job-shop scheduling problems. Aiming at these limitations, a new hybrid genetic algorithm called grafted genetic algorithm (GGA) is developed for job-shop scheduling problem. The introductions of grafted population and crossover probability matrix enhance the ability of the GGA to accelerate the evolvement and to fight premature convergence. Finally, the effectiveness and high efficiency are illustrated with the 6 classic examples.