most citedUsing Affine Combinations of BBOB Problems for Performance Assessment

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.NE2024

Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems

Haoran Yin, Diederick Vermetten, Furong Ye +2

When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be don…

cs.NE2023

When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems

André Thomaser, Jacob de Nobel, Diederick Vermetten +3

The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather im…

cs.NE20231 cited

Using Affine Combinations of BBOB Problems for Performance Assessment

Diederick Vermetten, Furong Ye, Carola Doerr

Benchmarking plays a major role in the development and analysis of optimization algorithms. As such, the way in which the used benchmark problems are defined significantly affects…

cs.NE2023

Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms

Furong Ye, Frank Neumann, Jacob de Nobel +2

Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algor…

cs.AI20231 cited

Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler

Frank Neumann, Aneta Neumann, Chao Qian +7

Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been sh…