How Are Genetic Algorithms Different than Other Optimizers?
Most optimization techniques used today are simple “exhaustive search” methods, meaning that every possible combination is tried to see what was the best one. This is a very accurate approach, since you are bound to find the best combination of variables – eventually. However, it is a very inefficient approach, because whenever there are more than a few thousand combinations, it takes too long to try them all. That is why users of exhaustive search optimizers tend to limit the number of variables they use, or tend to limit the number of values these variables can take. The genetic algorithm, by contrast, does not try every possible combination. It attempts instead to intelligently get closer and closer to the best solution. Therefore, far more variables can be utilized, and you can allow all values of a variable. Optimization can still take a good deal of time if you give a GA a fair number of variables, but it will be doing much more work in that amount of time. More efficient optimiz
