19 citations · 22 across the 19 of their papers we have counts for
9 papers · 1 filter
Integer Natural Evolution Strategies
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
While contemporary Evolution Strategies handle integer optimization problems effectively, their adaptation mechanism is grounded in -based Gaussian models, which are not na…
Evolving Executable Pipeline Programs for AutoML with Language Models
Sofoklis Kitharidis, Cor J. Veenman, Jan N. van Rijn +2
Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in advance: they can select and…
Sampling on Random Subspaces under Limited Data in the Context of Exploratory Landscape Analysis
Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1
Classical space-filling designs often fail to provide reliable statistical results for Exploratory Landscape Analysis (ELA) when only limited evaluation budgets are available, as c…
LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms
Georgios Laskaris, Reuben Brasher, Niki van Stein +3
Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent a…
3DMorph: Single-Image-Guided Local 3D Shape Editing and Morphing
Tobias Preintner, Yunfei Deng, Phillip Müller +5
Despite recent progress in 3D generation, intuitive editing of existing shapes remains limited. Unlike images, which benefit from well-established inpainting tools, general 3D obje…
Block-Bench: A Framework for Controllable and Transparent Discrete Optimization Benchmarking
Furong Ye, Frank Neumann, Thomas Bäck +1
We present a novel approach for constructing discrete optimization benchmarks that enables fine-grained control over problem properties, and such benchmarks can facilitate analyzin…
Optimization is Not Enough: Why Problem Formulation Deserves Equal Attention
Iván Olarte Rodríguez, Gokhan Serhat, Mariusz Bujny +3
Black-box optimization is increasingly used in engineering design problems where simulation-based evaluations are costly and gradients are unavailable. In this context, the optimiz…
Investigating the Interplay of Parameterization and Optimizer in Gradient-Free Topology Optimization: A Cantilever Beam Case Study
Jelle Westra, Iván Olarte Rodríguez, Niki van Stein +2
Gradient-free black-box optimization (BBO) is widely used in engineering design and provides a flexible framework for topology optimization (TO), enabling the discovery of high-per…
LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties
Urban Skvorc, Niki van Stein, Moritz Seiler +3
Benchmarking in continuous black-box optimisation is hindered by the limited structural diversity of existing test suites such as BBOB. We explore whether large language models emb…