some advantages of this approach are that it searches from population to population rather than point to point, and this makes it less likely to be trapped at local optima The population also preserves a number of valid solutions rather than converging to only one The disadvantages are long computation times due to the large number of objective func- tion evaluations required. Nevertheless it is more efficient than random walk or exhaus- tive search algorithms. All other optimization approaches listed here deal only with di- mensional synthesis, but genetic algorithms can also deal with type synthesis.