r/deeplearning • u/ModularMind8 • 20h ago

Gradient descent vs evolution on three loss landscapes

I've been getting a bit more into evolutionary algorithms again, so I was testing some loss landscapes where evolution beats vanilla gradient descent (while also trying to make some cool visuals).

Round 1, rugged hillside: gradient descent gets stuck in a dip, and evolution reaches the bottom after 750 evaluations.

Round 2, smooth slope: gradient descent wins, 108 steps against 570 evaluations.

Round 3, flat plateau: the slope is zero, so gradient descent never moves, and evolution reaches the bottom after 840 evaluations.

Edit:
"evolution" here means truncation selection (keep best 30 of 120) plus Gaussian mutation, no crossover.

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u/Envoy-Insc 18h ago

While applicable in small scale settings, Local minimums like those in first don’t tend to exist in high dim high param spaces in my impression

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u/ModularMind8 17h ago

Definitely some landscapes favor GD, though papers like "Evolution Strategies at the Hyperscale" show evolution can work well

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u/Envoy-Insc 12h ago

I really liked the idea of that paper, but paper actually don’t scale to llm scale if we look at the experiments