Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble

In MOO, there is usually not a single optimal solution, but a range of so-called Pareto optimal or non-dominated Many real-world black-box optimization problems solutions with different trade-offs. A widely adopted approach have multiple conflicting objectives. Rather aims to search for a good representation of these than attempting to approximate the entire set of Pareto-optimal solutions by maximizing their hypervolume. Pareto-optimal solutions, interactive preference Two prominent methods stand out in this regard: ParEGO learning, i.e., optimization with a decision maker (Knowles, 2006), which employs random augmented Chebyshev in the loop, allows to focus the search on the scalarizations for optimization in each iteration, and most relevant subset. However, few previous studies expected hypervolume maximization (Yang et al., 2019; have exploited the fact that utility functions Daulton et al., 2020), which directly maximizes the hypervolume are usually monotonic.