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Guided evolutionary strategies: augmenting random search with surrogate gradients

ICML 2019 · Optimization › Optimizers · approach: Differentiating through embedded optimization · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use Guided ES, which combines surrogate-gradient guidance with evolutionary-strategy searchQuadratic function with a biased gradient · Random quadratic optimizationloss suboptimalityHelped
Use Guided ES instead of vanilla ESQuadratic function with a biased gradient · Random quadratic optimizationloss suboptimalityHelped
Use Guided ES instead of CMA-ESQuadratic function with a biased gradient · Random quadratic optimizationloss suboptimalityHelped
Use Guided ES instead of vanilla ESUnrolled optimization · Learning-rate prediction with truncated backpropagationdistance from optimal learning rateHelped
Use Guided ES instead of SGDUnrolled optimization · Learning-rate prediction with truncated backpropagationdistance from optimal learning rateHelped
Use Guided ES with online synthetic-gradient guidanceSynthetic-gradient optimization · Target quadratic optimizationlossHelped
Use Guided ES instead of vanilla ESSynthetic-gradient optimization · Target quadratic optimizationlossHelped

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