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Genetic algorithms mimic clipped gradient descent in high-dimensional AI search

By PulseAugur Editorial · [1 sources] ·

Researchers have demonstrated that genetic algorithms can effectively function as a form of clipped gradient descent in high-dimensional search spaces. This process involves mutation-selection mechanisms that implicitly follow the gradient of the loss function without direct calculation. While slower than traditional gradient descent due to noise, the genetic algorithm's performance is dictated by the effective rank of the loss function's Hessian, which can be significantly smaller than the total number of parameters, particularly in neural network loss landscapes. This characteristic may explain the scalability of genetic algorithms in complex, high-dimensional problems. AI

IMPACT Explains the scalability of genetic algorithms in high-dimensional AI search spaces, potentially informing future optimization techniques.

RANK_REASON Academic paper detailing a theoretical finding about the mechanics of genetic algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Genetic algorithms mimic clipped gradient descent in high-dimensional AI search

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Academic paper detailing a theoretical finding about the mechanics of genetic algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Stephen Whitelam ·

    Why can genetic algorithms work in high-dimensional search spaces?

    We show that the effective dynamics of the elitist $(1+M)$ genetic algorithm is, in the limit of small mutations, clipped gradient descent on the loss in the presence of anisotropic Gaussian white noise. In expectation, therefore, a simple mutation-selection genetic algorithm fol…

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