 |
While researching optimization algorithms for my filter work, I came across the idea of genetic optimization. As opposed to the usual optimization scheme of venturing a single guess and exploring nearby points, this method is in close analogy with nature’s most famous optimization routine, evolution via natural selection. Genetic optimization takes a large set of initial guesses, and “cross-breeds” them. First of all, this method is nice because it converges after fewer iterations (assuming parallel simulations are carried out simultaneously). Secondly, it is more likely to find global maxima, essentially because it casts a wider net. Check out the simple java program I wrote:
|
|---|
