59 citations · 144 across the 61 of their papers we have counts for
28 papers · 1 filter
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…
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…
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…
Assessing Reproducibility in Evolutionary Computation: A Case Study using Human- and LLM-based Assessment
Francesca Da Ros, Tarik Začiragić, Aske Plaat +2
Reproducibility is an important requirement in evolutionary computation, where results largely depend on computational experiments. In practice, reproducibility relies on how algor…
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Haoran Yin, Shuaiqun Pan, Zhao Wei +5
The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critic…
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…
Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice
Kirill Antonov, Teus Tukker, Tiago Botari +3
Designing high-performance optical lenses entails exploring a high-dimensional, tightly constrained space of surface curvatures, glass choices, element thicknesses, and spacings. I…
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
Niki van Stein, Haoran Yin, Anna V. Kononova +2
We investigate the behaviour space of meta-heuristic optimisation algorithms automatically generated by Large Language Model driven algorithm discovery methods. Using the Large Lan…
Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems
Dikshit Chauhan, Bapi Dutta, Indu Bala +3
Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm design, and adaptive decision-making. Th…
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
Haoran Yin, Anna V. Kononova, Thomas Bäck +1
We study how large language models can be used in combination with evolutionary computation techniques to automatically discover optimization algorithms for the design of photonic…
Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms
Niki van Stein, Anna V. Kononova, Lars Kotthoff +1
Large Language Models (LLMs) have demonstrated great promise in generating code, especially when used inside an evolutionary computation framework to iteratively optimize the gener…
Stalling in Space: Attractor Analysis for any Algorithm
Sarah L. Thomson, Quentin Renau, Diederick Vermetten +3
Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisation alg…
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
Haoran Yin, Anna V. Kononova, Thomas Bäck +1
The integration of Large Language Models (LLMs) with evolutionary computation (EC) has introduced a promising paradigm for automating the design of metaheuristic algorithms. Howeve…
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
Niki van Stein, Diederick Vermetten, Thomas Bäck
Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks. How…
Selection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms
Kirill Antonov, Marijn Siemons, Niki van Stein +3
This work addresses the critical challenge of optimal filter selection for a novel trace gas measurement device. This device uses photonic crystal filters to retrieve trace gas con…
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems
Ryan Zhou, Jaume Bacardit, Alexander Brownlee +7
Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response…
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…
A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
Niki van Stein, Sarah L. Thomson, Anna V. Kononova
To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on…
Explainable Benchmarking for Iterative Optimization Heuristics
Niki van Stein, Diederick Vermetten, Anna V. Kononova +1
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristi…
A Functional Analysis Approach to Symbolic Regression
Kirill Antonov, Roman Kalkreuth, Kaifeng Yang +3
Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although trad…
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Kirill Antonov, Anna V. Kononova, Thomas Bäck +1
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics. However, in practical applications, it is not likely to always find…
Challenges of ELA-guided Function Evolution using Genetic Programming
Fu Xing Long, Diederick Vermetten, Anna V. Kononova +4
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysi…
Deep-BIAS: Detecting Structural Bias using Explainable AI
Bas van Stein, Diederick Vermetten, Fabio Caraffini +1
Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced…