title: Parameter Optimization in Python
body: <p>Given a Python callable that returns a fitness measurement (0.0=horrible, 0.5=ok, 1.0=perfect) and a description of its parameters (type=bool|int|float|nominal, min, max), what are robust implementations of parameter optimizers that can find the combination of parameters that get the fitness measure as high as possible? I'm not looking for an exhaustive guaranteed global optimum. An approximation would be fine.</p>
  
  <p>I've seen scipy's <a href="http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html" rel="nofollow">optimize</a> module referenced a lot, but also scikit-learn's <a href="http://scikit-learn.org/stable/modules/grid_search.html" rel="nofollow">gridsearch</a>. What's the practical difference between these two? What are other options?</p>