SILO outperforms five baselines on eight protein fitness landscapes by using trajectory-level imitation on trajectories selected via hierarchical beam search and biological proxy guidance under limited oracle budgets.
Empirical fitness landscapes and the predictability of evolution , Volume =
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In isotropic Gaussian random field fitness landscapes the expected number of local optima is determined by the correlation of fitness effects, with modularity increasing and locus heterogeneity decreasing the count.
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Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
SILO outperforms five baselines on eight protein fitness landscapes by using trajectory-level imitation on trajectories selected via hierarchical beam search and biological proxy guidance under limited oracle budgets.
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Epistatic strength, modularity, and locus heterogeneity shape the number of local optima in fitness landscapes
In isotropic Gaussian random field fitness landscapes the expected number of local optima is determined by the correlation of fitness effects, with modularity increasing and locus heterogeneity decreasing the count.