14 citations · 23 across the 11 of their papers we have counts for
11 papers
Exposing Blind Spots in Deep Imbalanced Regression Evaluation
Noah C. Puetz, Jens U. Brandt, Marc Hilbert +3
Deep Imbalanced Regression (DIR) addresses a common failure mode of regression models: target distributions are highly non-uniform, causing models to perform best in densely popula…
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1
Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsit…
Structural bias in multi-objective optimisation
Jakub Kudela, Niki van Stein, Thomas Bäck +1
Structural bias (SB) refers to systematic preferences of an optimisation algorithm for particular regions of the search space that arise independently of the objective function. Wh…
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks
Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez +2
Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. However, constructing a high-quality surrogate model often deman…
MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler
Diederick Vermetten, Jeroen Rook, Oliver L. Preuß +5
Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anyti…
Multi-Step Reasoning with Large Language Models, a Survey
Aske Plaat, Annie Wong, Suzan Verberne +3
Large language models (LLMs) with billions of parameters exhibit in-context learning abilities, enabling few-shot learning on tasks that the model was not specifically trained for.…
CGP++ : A Modern C++ Implementation of Cartesian Genetic Programming
Roman Kalkreuth, Thomas Baeck
The reference implementation of Cartesian Genetic Programming (CGP) was written in the C programming language. C inherently follows a procedural programming paradigm, which entails…
Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem
Saba Sadeghi Ahouei, Jacob de Nobel, Aneta Neumann +2
Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance…
LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics
Niki van Stein, Thomas Bäck
Large Language Models (LLMs) such as GPT-4 have demonstrated their ability to understand natural language and generate complex code snippets. This paper introduces a novel Large La…
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
Diederick Vermetten, Furong Ye, Thomas Bäck +1
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics. In continuous, single-objective optimization, severa…
DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
Bas van Stein, Fu Xing Long, Moritz Frenzel +3
We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e.g., automated selection…