Outputs (37)

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.

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.

A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories (2024)
Presentation / Conference Contribution
van Stein, N., Thomson, S. L., & Kononova, A. V. (2024, September). A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories. Presented at Parallel Problem Solving from Nature (PPSN) 2024, Hagenberg, Austria

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 a wide variety of search landscapes. This study explores the impact o... Read More about A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories.

Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment (2024)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., van den Berg, D., Liang, T., & Weise, T. (2024, September). Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment. Presented at Parallel Problem Solving from Nature (PPSN 2024), Hagenberg, Austria

Local optima are a menace that can trap optimisation processes. Frequency fitness assignment (FFA) is an concept aiming to overcome this problem. It steers the search towards solutions with rare fitness instead of high-quality fitness. FFA-based algo... Read More about Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment.

Information flow and Laplacian dynamics on local optima networks (2024)
Presentation / Conference Contribution
Richter, H., & Thomson, S. L. (2024, June). Information flow and Laplacian dynamics on local optima networks. Presented at IEEE Congress on Evolutionary Computation (IEEE CEC), Yokohama, Japan

We propose a new way of looking at local optima networks (LONs). LONs represent fitness landscapes; the nodes are local optima, and the edges are search transitions between them. Many metrics computed on LONs have been proposed and shown to be linked... Read More about Information flow and Laplacian dynamics on local optima networks.

The Easiest Hard Problem: Now Even Easier (2024)
Presentation / Conference Contribution
Horn, R., Thomson, S. L., van den Berg, D., & Adriaans, P. (2024, July). The Easiest Hard Problem: Now Even Easier. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We present an exponential decay function that characterizes the number of solutions to instances of the Number Partitioning Problem (NPP) with uniform distribution of bits across the integers. This function is fitted on the number of optimal solution... Read More about The Easiest Hard Problem: Now Even Easier.

Exploring the use of fitness landscape analysis for understanding malware evolution (2024)
Presentation / Conference Contribution
Babaagba, K., Murali, R., & Thomson, S. L. (2024, July). Exploring the use of fitness landscape analysis for understanding malware evolution. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We conduct a preliminary study exploring the potential of using fitness landscape analysis for understanding the evolution of malware. This type of optimisation is fairly new and has not previously been studied through the lens of landscape analysis.... Read More about Exploring the use of fitness landscape analysis for understanding malware evolution.

Explaining evolutionary feature selection via local optima networks (2024)
Presentation / Conference Contribution
Adair, J., Thomson, S. L., & Brownlee, A. E. I. (2024, July). Explaining evolutionary feature selection via local optima networks. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We analyse fitness landscapes of evolutionary feature selection to obtain information about feature importance in supervised machine learning. Local optima networks (LONs) are a compact representation of a landscape, and can potentially be adapted fo... Read More about Explaining evolutionary feature selection via local optima networks.