title: How to train a neural network to supervised data set using pybrain black-box optimization?
body: <p>I have played around a bit with pybrain and understand how to generate neural networks with custom architectures and train them to supervised data sets using backpropagation algorithm. </p>
  
  <p>However I am confused by the optimization algorithms and the concepts of tasks, learning agents and environments. </p>
  
  <p>For example:
  How would I implement a neural network such as (1) to classify the XOR dataset using pybrain genetic algorithm (2)?</p>
  
  <p>(1) <code>pybrain.tools.shortcuts.buildNetwork(2, 3, 1)</code></p>
  
  <p>(2) <code>pybrain.optimization.GA()</code></p>