How can I improve Bayesian optimization techniques?

How can I improve Bayesian optimization techniques?

You can improve Bayesian optimization by tuning the acquisition function, using better surrogate models, and incorporating parallel evaluations.

What are some common acquisition functions I should consider?

Common acquisition functions include Expected Improvement (EI), Probability of Improvement (PI), and Upper Confidence Bound (UCB). Each has its strengths depending on your optimization goals.

How do I choose the right surrogate model?

Choosing the right surrogate model depends on the nature of your objective function. Gaussian Processes are popular for their flexibility, while Random Forests can handle high-dimensional spaces well.

Can you explain how to implement parallel evaluations?

To implement parallel evaluations, you can use multi-fidelity optimization or batch Bayesian optimization, which allows you to evaluate multiple points simultaneously.