11 Optimizing multiple objectives at the same time

 

This chapter covers

  • The problem of optimizing multiple objectives at the same time
  • Training multiple GPs to learn about multiple objectives at the same time
  • Jointly optimizing multiple objectives

Every day, we are faced with optimization tradeoffs:

  • “This coffee tastes good, but there’s too much sugar.”
  • “That shirt looks great, but it’s out of my price range.”
  • “The neural network I just trained has a high accuracy, but it is too big and takes too long to train.”

11.1 Balancing multiple optimization objectives with BayesOpt

11.2 Finding the boundary of the most optimal data points

11.3 Seeking to improve the optimal data boundary

11.4 Exercise: Multiobjective optimization of airplane design

Summary