All these meta heuristic type things (simulated annealing, GAs,...) are really just fancy dress on MCMC adapted for mode seeking (usually via importance samplin

All these meta heuristic type things (simulated annealing, GAs,...) are really just fancy dress on MCMC adapted for mode seeking (usually via importance sampling by scaling). Specifically in this case the evolution bit is a sampler and evaluation function is the log likelihood. Nothing wrong with that, MCMC is awesome. But you need to put in the features of your particular problem if you want good performance. i.e. if your input to cost surface mapping is continuous and you can efficiently calculate a gradient then you would be silly not to use it. Your sampler will be much more efficient.