Name
Abstract | ||
|---|---|---|
•Predictive entropy search is applied to the constrained multi-objective scenario.•The technique reduces the entropy of the Pareto set in the feasible space.•The acquisition function is approximated using expectation propagation.•Empirical results show that the proposed technique outperforms other methods.•The proposed technique allows for decoupled evaluation scenarios. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2019.06.025 | Neurocomputing |
Keywords | Field | DocType |
Bayesian Optimization,Constrained Multi-Objective Scenario,Information theory | Stochastic optimization,Mathematical optimization,Global optimization,Vector optimization,Test functions for optimization,Bayesian optimization,Multi-objective optimization,Artificial intelligence,Random optimization,Machine learning,Mathematics,Metaheuristic | Journal |
Volume | ISSN | Citations |
361 | 0925-2312 | 3 |
PageRank | References | Authors |
0.42 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. Garrido | 1 | 10 | 2.27 |
| Daniel Hernández-Lobato | 2 | 440 | 26.10 |