The Parallel Genetic Algorithm Embedded with Downhill

This paper introduces and analyzes the algorithm of Parallel Genetic Alogrithm (PGA) embedded with Downhill which is applied to the optimization of continuous functions. The strategy goes this way: Subpopulation tries to locate good local minima by PGA. When a subpopulation does not progress after a certain number of generations, downhill is taken into account. Alternative downhill method (depth first or width first) is to be used depending on the properties of different problems. At certain generation, local optima of each nodes are transmitted to each other with the support of MPI environment. Selection of genetic factors is discussed and comparison with traditional GA is made to illustrate the effectiveness of the algorithm. On the whole, the hybrid algorithm strives to gain breakthrough in the field of large scale computation and as a matter of fact it turns out to be quite successful.