2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.NE2024★ 2 cited
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…
cs.AI2024
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…