42 citations · 57 across the 7 of their papers we have counts for
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Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
Mohammad Nabi Omidvar, Danial Yazdani, Juergen Branke +3
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.…
One Step Preference Elicitation in Multi-Objective Bayesian Optimization
Juan Ungredda, Mariapia Marchi, Teresa Montrone +1
We consider a multi-objective optimization problem with objective functions that are expensive to evaluate. The decision maker (DM) has unknown preferences, and so the standard app…
Bayesian Optimisation for Constrained Problems
Juan Ungredda, Juergen Branke
Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functi…