104 citations · 367 across the 54 of their papers we have counts for
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The Modular CMA-ES: A Framework for Modern Evolution Strategies
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), have become established as powerful methods for continu…
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…
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…
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…
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…
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…
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…
Interactive embodied evolution for socially adept Artificial General Creatures
Kevin Godin-Dubois, Olivier Weissl, Karine Miras +1
We introduce here the concept of Artificial General Creatures (AGC) which encompasses "robotic or virtual agents with a wide enough range of capabilities to ensure their continued…
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…
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…
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…
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…
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Diederick Vermetten, Manuel López-Ibáñez, Olaf Mersmann +2
Box-constraints limit the domain of decision variables and are common in real-world optimization problems, for example, due to physical, natural or spatial limitations. Consequentl…
Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite
Mădălina-Andreea Mitran, Anna V. Kononova, Fabio Caraffini +1
This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifyin…