Machine-Assisted Adaptive Selection Index
This module demonstrates a learning-based weight update mechanism, where selection weights evolve automatically based on observed gain, mirroring gradient-based optimization in AI systems.
Weight Update Rule
bᵢ(t+1) = bᵢ(t) + η · ∂G(t) / ∂bᵢ
Interactive Adaptive Weight Update Simulator
| Trait (i) | bᵢ(t) | ∂G(t)/∂bᵢ | bᵢ(t+1) |
|---|
Interpretation:
Positive sensitivity increases trait importance, negative sensitivity reduces it. The learning rate controls adaptation speed and stability.
Positive sensitivity increases trait importance, negative sensitivity reduces it. The learning rate controls adaptation speed and stability.
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