title: Multiple parameter optimization with lots of local minima
body: <p>I'm looking for algorithms to find a "best" set of parameter values. The function in question has a lot of local minima and changes very quickly. To make matters even worse, testing a set of parameters is very slow - on the order of 1 minute - and I can't compute the gradient directly.</p>
  
  <p>Are there any well-known algorithms for this kind of optimization?</p>
  
  <p>I've had moderate success with just trying random values. I'm wondering if I can improve the performance by making the random parameter chooser have a lower chance of picking parameters close to ones that had produced bad results in the past. Is there a name for this approach so that I can search for specific advice?</p>
  
  <p>More info:</p>
  
  <ul>
  <li>Parameters are continuous</li>
  <li>There are on the order of 5-10 parameters. Certainly not more than 10.</li>
  </ul>