I am trying to optimize some force-field parameters in a molecular framework so that the result of simulation comes as close as it can to the experimental structure.
In the past, I have written a genetic algorithm where I essentially randomly sample the parameter space, select the combination that works best, create sets of mutated parameters, and repeat the process until I get the best parameters for some objective function. I also perform some optimization of the algorithm itself, where the distribution of the mutated values is optimized to favor faster convergence.
My advisor has not heard of genetic algorithms, and I have never heard of the methods he recommended: conjugate gradient method and the simplex algorithm.
In my situation, the objective function is a function of every atom's deviation from its experimental location (so its a structural optimization). The system is 4-10K atoms. Is it worth it to invest some time into learning CGM or the simplex algorithm? Out of all three, which is the best for this situation?