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