42 citations · 60 across the 8 of their papers we have counts for
9 papers
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…
Reproducibility in Evolutionary Computation
Manuel López-Ibáñez, Juergen Branke, Luís Paquete
Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflec…
Exploiting Transitivity for Top-k Selection with Score-Based Dueling Bandits
Matthew Groves, Juergen Branke
We consider the problem of top-k subset selection in Dueling Bandit problems with score information. Real-world pairwise ranking problems often exhibit a high degree of transitivit…
Bayesian Optimisation vs. Input Uncertainty Reduction
Juan Ungredda, Michael Pearce, Juergen Branke
Simulators often require calibration inputs estimated from real world data and the quality of the estimate can significantly affect simulation output. Particularly when performing…