Sarah L. Thomson
sarah l thomson

Dr Sarah L. Thomson

Lecturer

  S.Thomson4@napier.ac.uk

  

Biography

Dr Sarah L. Thomson is a lecturer in data science at Edinburgh Napier University, having started there in August 2023. She was previously at the University of Stirling: she was awarded her PhD there in March 2021, and worked as a research fellow from September 2020 until May 2022; after that. she took up a lectureship in June 2022, before moving to Edinburgh Napier in August 2023.

Dr Thomson's expertise is predominantly in evolutionary computation, optimisation, and machine learning. She has worked on problems from healthcare, aviation, vehicle management, and agriculture. Additionally, she has a passion for fundamental research and is particularly interested in fitness landscapes and explainable artificial intelligence (XAI).

School

Research Groups

Research Areas

Filter by

Date


7 results

Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective

Conference Proceeding
Rodriguez, C. J., Thomson, S. L., Alderliesten, T., & Bosman, P. A. N. (in press)
Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective. . https://doi.org/10.1145/3638529.3654125
Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle suc...

Understanding fitness landscapes in morpho-evolution via local optima networks

Conference Proceeding
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (in press)
Understanding fitness landscapes in morpho-evolution via local optima networks. . https://doi.org/10.1145/3638529.3654059
Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings h...

Information flow and Laplacian dynamics on local optima networks

Conference Proceeding
Richter, H., & Thomson, S. L. (in press)
Information flow and Laplacian dynamics on local optima networks.
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 th...

Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances

Conference Proceeding
Verel, S., Thomson, S. L., & Rifki, O. (in press)
Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances.
The Quadratic Assignment Problem (QAP) is one of the major domains in the field of evolutionary computation, and more widely in combinatorial optimization. This paper studies ...

Can HP-protein folding be solved with genetic algorithms? Maybe not

Conference Proceeding
Jansen, R., Horn, R., van Eck, O., Version, K., Thomson, S. L., & van den Berg, D. (2023)
Can HP-protein folding be solved with genetic algorithms? Maybe not. In Proceedings of the 15th International Joint Conference on Computational Intelligence (131-140). https://doi.org/10.5220/0012248500003595
Genetic algorithms might not be able to solve the HP-protein folding problem because creating random individuals for an initial population is very hard, if not impossible. The...

The Opaque Nature of Intelligence and the Pursuit of Explainable AI

Conference Proceeding
Thomson, S. L., van Stein, N., van den Berg, D., & van Leeuwen, C. (2023)
The Opaque Nature of Intelligence and the Pursuit of Explainable AI. In Proceedings of the 15th International Joint Conference on Computational Intelligence (555-564). https://doi.org/10.5220/0012249500003595
When artificial intelligence is used for making decisions, people are more likely to accept those decisions if they can be made intelligible to the public. This understanding ...

Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem.

Conference Proceeding
Verduin, K., Thomson, S. L., & van den Berg, D. (2023)
Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. In Proceedings of the 15th International Joint Conference on Computational Intelligence (246-257). https://doi.org/10.5220/0012192100003595
Unlike other NP-hard problems, the constraints on the traveling tournament problem are so pressing that it’s hardly possible to randomly generate a valid solution, for example...

Current Post Grad projects