2 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation
C. J. Rodriguez, S. L. Thomson, T. Alderliesten +1
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 such pro…
Understanding fitness landscapes in morpho-evolution via local optima networks
Sarah L. Thomson, Léni K. Le Goff, Emma Hart +1
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 have b…
Information flow and Laplacian dynamics on local optima networks
Hendrik Richter, Sarah L. Thomson
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. M…