Understanding fitness landscapes in morpho-evolution via local optima networks

• URL: http://arxiv.org/abs/2402.07822v1
• Date: Mon, 12 Feb 2024 17:26:35 GMT
• Title: Understanding fitness landscapes in morpho-evolution via local optima networks
• Authors: Sarah L. Thomson, L\'eni K. Le Goff, Emma Hart, Edgar Buchanan
• Abstract summary: Morpho-evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. 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 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.
• Score: 0.1843404256219181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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