40 citations · 58 across the 26 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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…
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception. Many lines of reasoning have been given for this strong dependen…
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…
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…
Sampling in CMA-ES: Low Numbers of Low Discrepancy Points
Jacob de Nobel, Diederick Vermetten, Thomas H. W. Bäck +1
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is one of the most successful examples of a derandomized evolution strategy. However, it still relies on randomly sampl…
Avoiding Redundant Restarts in Multimodal Global Optimization
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Naïve restarts of global optimization solvers when operating on multimodal search landscapes may resemble the Coupon's Collector Problem, with a potential to waste significant func…
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…
Large-scale Benchmarking of Metaphor-based Optimization Heuristics
Diederick Vermetten, Carola Doerr, Hao Wang +2
The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community. One partic…
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…
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…