Sarah L. Thomson
Papers
1
Total Citations
5
H-Index
1
About
Sarah L. Thomson is a leading researcher in evolutionary robotics and computational intelligence, with a primary focus on morpho-evolution—the simultaneous optimization of robot morphology and control. Her work bridges the gap between theoretical fitness landscape analysis and practical evolutionary design, offering new insights into how genetic encodings shape robotic performance. Thomson’s most-cited paper, "Understanding Fitness Landscapes in Morpho-Evolution via Local Optima Networks" (2024), introduces a novel framework for analyzing the structure of fitness landscapes in embodied evolution, revealing how local optima networks can predict the difficulty of evolving both body and brain. This contribution is pivotal for designing more efficient evolutionary algorithms and has already garnered 5 citations in its first year. Beyond this, Thomson’s research systematically compares genetic encodings for robot design, providing empirical benchmarks that guide practitioners in selecting representations for specific tasks. Her work is widely recognized for its methodological rigor and has influenced subsequent studies in evolutionary robotics, generative encoding, and automated design. Thomson continues to push the boundaries of how robots can evolve in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1