40 citations · 52 across the 14 of their papers we have counts for
14 papers
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…
Distributed Resource Selection for Self-Organising Cloud-Edge Systems
Quentin Renau, Amjad Ullah, Emma Hart
This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands o…
Class Incremental Learning for Algorithm Selection
Mate Botond Nemeth, Emma Hart, Kevin Sim +1
Algorithm selection is commonly used to predict the best solver from a portfolio per per-instance. In many real scenarios, instances arrive in a stream: new instances become availa…
Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model
Quentin Renau, Emma Hart
Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is algorithm-centric in order to encapsulate…
Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing
Kevin Sim, Quentin Renau, Emma Hart
Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing methods,…
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…
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
Quentin Renau, Emma Hart
Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an…
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
Emma Hart, Quentin Renau, Kevin Sim +1
Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representations…
Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples
Quentin Renau, Emma Hart
The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selectio…
On the Utility of Probing Trajectories for Algorithm-Selection
Quentin Renau, Emma Hart
Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance descripti…
Automated Algorithm Selection for Radar Network Configuration
Quentin Renau, Johann Dreo, Alain Peres +3
The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as…
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
Quentin Renau, Johann Dreo, Carola Doerr +1
Facilitated by the recent advances of Machine Learning (ML), the automated design of optimization heuristics is currently shaking up evolutionary computation (EC). Where the design…
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes
Tome Eftimov, Gorjan Popovski, Quentin Renau +2
Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning,…
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
Quentin Renau, Carola Doerr, Johann Dreo +1
Exploratory landscape analysis (ELA) supports supervised learning approaches for automated algorithm selection and configuration by providing sets of features that quantify the mos…