Hybrid optimization-an experimental study

The authors compare the performance of a hybrid optimization method to that of pure gradient based methods. The hybrid optimization method comprises an initial adaptive ordinal search phase followed by a gradient ascent (descent) phase. The adaptive ordinal search phase consists of fixing the size of the design population and ranking the members of the population using an estimated value of the performance. Members of the design population for the next stage are picked using the top designs of the previous population. This process is achieved via a variation on the standard genetic algorithm (see D. E. Goldberg, 1989). Ho et al. (1992) showed that ranks of populations are relatively insensitive to simulation noise, and as the experimental data show, this fact is useful in using short simulation runs to improve the search efficiency before the onset of the final gradient ascent (descent) phase.<
>