Bio-inspired algorithms offer a compelling narrative for adaptive AI, yet they often mask a lack of rigorous mathematical foundation with poetic animal metaphors. It is a brilliant exercise in biomimicry that simplifies complex optimization at the cost of theoretical depth.
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How AI can learn from animal behavior #shorts #tedxAdded:
Wolves are among the most organized hunting creatures out there.
They constantly search their surrounding to find food and ensure their survival.
And that's why I'd like to call them the best search agent because they can pretty much search any sort of landscape. And that hits me.
I thought, "What if I could create an algorithm this way?"
The first step I took was to better understand the main principles of their behaviors, which are decision-making, hunting, and navigation. I started by developing mathematical models for these using data collected by observing wolves or via sensors. Then, I turned those models into series of processes and steps that could be represented computationally. And that finally led to the development of an optimization algorithm called the grey wolf optimizer.
Unlike the traditional algorithm that heavily rely on predefined rules, this wolf-inspired algorithm allows AI to dynamically adapt based on the interaction of those wolves with the problem.
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