Fig. 5: Handling of input constraints using pre- and post-repair. | npj Computational Materials

Fig. 5: Handling of input constraints using pre- and post-repair.

From: Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

Fig. 5

a Example of a 2D optimization towards a single optimum (blue star). In post-repair mode, stochastic sample points (black disks) are all considered during the optimization process, best candidates (red disks) are then repaired (gray line) to the nearest feasible point. In pre-repair mode, stochastic samples are first repaired (gray line) by projection to the feasible space, and the best repaired candidates (red disks) are selected. b Location of dominated (grey disks) and non-dominated (black squares) points in the decision and objective space after a single run of EGBO with pre-repair. c Performance metrics for pure qNEHVI-BO (blue) and EGBO (orange), with dotted lines for post-repair and solid lines for pre-repair demonstrating the superiority of EGBO. We also include Sobol sampling (grey) as a baseline comparison.

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