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Building resilience is of the foremost interest, and it is an endeavor to which AI methods in general and bioinspired algorithms in particular can be of huge relevance: they can be instrumental in analyzing and understanding the threats, risks and weaknesses of the system, and they can contribute to the optimization of said systems, the improvement of disruption preparedness, and the design of restoration strategies, therefore engineering resilience into the system. Bioinspired methods are themselves subject to potential disruptions during their use, and therefore need resilience features to ensure their continued operation. The plasticity and flexibility of these techniques makes them particularly amenable to this augmentation. They are also readily adaptable to different optimization targets by embedding suitable problem-aware algorithmic components.
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