Bohachevsky, I, M Johnson, and M Stein. 1986. “Generalized Simulated Annealing for Function Optimization.” Technometrics 28 (3): 209–17.

Frazier, R. 2018. “A Tutorial on Bayesian Optimization.”

Kirkpatrick, S, D Gelatt, and M Vecchi. 1983. “Optimization by Simulated Annealing.” Science 220 (4598): 671–80.

Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

Rasmussen, C, and C Williams. 2006. Gaussian Processes for Machine Learning. Gaussian Processes for Machine Learning. MIT Press.

Schulz, E, M Speekenbrink, and A Krause. 2018. “A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions.” Journal of Mathematical Psychology 85: 1–16.

Shahriari, B., K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas. 2016. “Taking the Human Out of the Loop: A Review of Bayesian Optimization.” Proceedings of the IEEE 104 (1): 148–75.

Van Laarhoven, P, and E Aarts. 1987. “Simulated Annealing.” In Simulated Annealing: Theory and Applications, 7–15. Springer.

  1. This equation is also the same as the radial basis function used in kernel methods, such as the SVM model that is currently being used. This is a coincidence; this covariance function is unrelated to the SVM tuning parameter that we are using. ↩︎