Outputs (43)

Understanding the Navigation of Robot Morphology Spaces with Local Optima Network Analysis (2025)
Presentation / Conference Contribution
Thomson, S. L., Le Goff, L. K., Hart, E., Eiben, A. E., & Luck, K. S. (2025, October). Understanding the Navigation of Robot Morphology Spaces with Local Optima Network Analysis. Presented at ALife 2025, Kyoto, Japan

The task of co-optimising the morphology and control system of an embodied robot using evolutionary approaches has received increased attention in recent years, often using a Compositional Pattern Producing Network (CPPN) to encode the morphology. Ho... Read More about Understanding the Navigation of Robot Morphology Spaces with Local Optima Network Analysis.

Factors Impacting Landscape Ruggedness in Control Problems: a Case Study (2025)
Presentation / Conference Contribution
Saliby, M. E., Medvet, E., Nadizar, G., Salvato, E., & Thomson, S. L. (2024, September). Factors Impacting Landscape Ruggedness in Control Problems: a Case Study. Presented at WIVACE 2024 (XVIII International Workshop on Artificial Life and Evolutionary Computation), Namur, Belgium

Understanding fitness landscapes in evolutionary robotics (ER) can provide valuable insights into the considered robotic problems as well as into the strategies found by evolutionary algorithms (EAs) to address them, ultimately guiding practitioners... Read More about Factors Impacting Landscape Ruggedness in Control Problems: a Case Study.

Subfunction Structure Matters: A New Perspective on Local Optima Networks (2025)
Presentation / Conference Contribution
Thomson, S. L., & Przewozniczek, M. W. (2025, July). Subfunction Structure Matters: A New Perspective on Local Optima Networks. Presented at Genetic and Evolutionary Computation Conference (GECCO 2025), Málaga, Spain

Local optima networks (LONs) capture fitness landscape information. They are typically constructed in a black-box manner; information about the problem structure is not utilised. This also applies to the analysis of LONs: knowledge about the problem,... Read More about Subfunction Structure Matters: A New Perspective on Local Optima Networks.

Variable Importance Estimation for High-Dimensional Optimisation (2025)
Presentation / Conference Contribution
Hunter, K., Thomson, S. L., & Hart, E. (2025, September). Variable Importance Estimation for High-Dimensional Optimisation. Paper presented at 24th UK Workshop in Computational Intelligence (UKCI 2025), Edinburgh, United Kingdom

Machine learning models trained on the solution spaces of optimisation problems can potentially shed light on variable importance. In prior work the recently established combinatorial benchmark, Polynomial Unconstrained Binary Optimisation with varia... Read More about Variable Importance Estimation for High-Dimensional Optimisation.

XAI for Algorithm Configuration and Selection (2025)
Book Chapter
Thomson, S. L., Hart, E., & Renau, Q. (2025). XAI for Algorithm Configuration and Selection. In N. van Stein, & A. V. Kononova (Eds.), Explainable AI for Evolutionary Computation. Springer. https://doi.org/10.1007/978-981-96-2540-6_6

In this chapter, we consider, formalise, and demonstrate the ways in which XAI can assist or inform algorithm selection and configuration. Reviewing the literature, we notice and taxonomise a much broader and more diverse notion of XAI than is typica... Read More about XAI for Algorithm Configuration and Selection.

Into the Black Box: Mining Variable Importance with XAI (2025)
Presentation / Conference Contribution
Hunter, K., Thomson, S. L., & Hart, E. (2025, April). Into the Black Box: Mining Variable Importance with XAI. Presented at Evostar 2025, Trieste, Italy

Recent works have shown that the idea of mining search spaces to train machine learning models can facilitate increasing understanding of variable importance in optimisation problems. However , so far, the problems studied have typically either been... Read More about Into the Black Box: Mining Variable Importance with XAI.

Stalling in Space: Attractor Analysis for any Algorithm (2025)
Presentation / Conference Contribution
Thomson, S. L., Renau, Q., Vermetten, D., Hart, E., van Stein, N., & Kononova, A. V. (2025, April). Stalling in Space: Attractor Analysis for any Algorithm. Paper presented at EvoStar 2025, Trieste, Italy

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 algorithms. Local optima networks (LONs) have been especially dominant in... Read More about Stalling in Space: Attractor Analysis for any Algorithm.

Evaluating the Application and Performance of Regression Models in Predicting Cycling Power Output (2025)
Presentation / Conference Contribution
Walker, E., Bamgboye, O., Thomson, S. L., & Liu, X. (2025, July). Evaluating the Application and Performance of Regression Models in Predicting Cycling Power Output. Paper presented at COMPSAC 2025: IEEE Computers, Software, and Applications Conference, Toronto, Canada

Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem (2024)
Presentation / Conference Contribution
Chen, J., Wu, Z., Thomson, S. L., & Weise, T. (2024, November). Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. Presented at ECTA 2024: 16th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal

The Quadratic Assignment Problem (QAP) is one of the classical N P-hard tasks from operations research with a history of more than 65 years. It is often approached with heuristic algorithms and over the years, a multitude of such methods has been app... Read More about Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem.

The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes (2024)
Presentation / Conference Contribution
Pijning, I., Koppenhol, L., Dijkzeul, D., Brouwer, N., Thomson, S. L., & van den Berg, D. (2024, November). The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes. Presented at ECTA 2024: 16th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal

The Frequency Fitness Assignment (FFA) method steers evolutionary algorithms by objective rareness instead of objective goodness. Does this mean the size of the combinatorial search space influences its performance when compared to more traditional e... Read More about The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes.