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fitness-landscape

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GFlowNets for in-silico directed evolution: sampling diverse, feasible, high-fitness sequence variants rather than hill-climbing to one. Exact-ground-truth benchmarks (Ehrlich functions, GB1), multi-objective rewards, and a design-build-test-learn campaign loop under an evaluation budget.

  • Updated Sep 4, 2026
  • Python

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