Understanding fitness landscapes in morpho-evolution via local optima networks
CoRR(2024)
摘要
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 been proposed which are capable of representing
design and control. Previous research has provided empirical comparisons
between encodings in terms of their performance with respect to an objective
function and the diversity of designs that are evaluated, however there has
been no attempt to explain the observed findings. We address this by applying
Local Optima Network (LON) analysis to investigate the structure of the fitness
landscapes induced by three different encodings when evolving a robot for a
locomotion task, shedding new light on the ease by which different fitness
landscapes can be traversed by a search process. This is the first time LON
analysis has been applied in the field of ME despite its popularity in
combinatorial optimisation domains; the findings will facilitate design of new
algorithms or operators that are customised to ME landscapes in the future.
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