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Found 4 papers, 0 papers with code
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables
Using adapted covariance models and dedicated enrichment strategy for the Gaussian processes in Bayesian optimization, this approach allows to reduce the computational cost up to two orders of magnitude, with respect to classical Quality-Diversity approaches while dealing with discrete choices and the presence of constraints.
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes
Gaussian Processes (GPs) are one of the popular approaches to exhibit the correlations between these different fidelity levels.
Bayesian optimization of variable-size design space problems
This results in an optimization problem for which the search space varies dynamically (with respect to both number and type of variables) along the optimization process as a function of the values of specific discrete decision variables.
Bayesian Optimization using Deep Gaussian Processes
To overcome this issue, a new Bayesian Optimization approach is proposed.
