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Found 7 papers, 0 papers with code
Bayesian Optimization using Deep Gaussian Processes
To overcome this issue, a new Bayesian Optimization approach is proposed.
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.
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.
Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities
The design process of complex systems such as new configurations of aircraft or launch vehicles is usually decomposed in different phases which are characterized for instance by the depth of the analyses in terms of number of design variables and fidelity of the physical models.
Hidden-Variables Genetic Algorithm for Variable-Size Design Space Optimal Layout Problems with Application to Aerospace Vehicles
In order to tackle this NP-hard problem, a genetic algorithm enhanced by an adapted hidden-variables mechanism is proposed.
A Virtual-Force Based Swarm Algorithm for Balanced Circular Bin Packing Problems
Balanced circular bin packing problems consist in positioning a given number of weighted circles in order to minimize the radius of a circular container while satisfying equilibrium constraints.
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.
