> ## Documentation Index > Fetch the complete documentation index at: https://proto.evodesign.org/docs/llms.txt > Use this file to discover all available pages before exploring further. # Rejection Sampling Optimizer > Draws many independent candidates from the generators, scores each against the constraints, and keeps the lowest-energy designs. Stateless and fully parallel, with no iterative refinement between samples.
each batch: draw B → score → keep the K lowest-energyp(x)xacceptedrejected
each batch: draw B → score → keep the K lowest-energyp(x)xacceptedrejected

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Source
evo-design/proto-language/proto\_language/optimizer/rejection\_sampling\_optimizer.py
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Rejection Sampling optimizer for sequence optimization through extensive sampling. Generates many proposal sequences and keeps only the best `num_results` by lowest energy score. Unlike iterative optimizers (MCMC, beam search), each proposal batch starts fresh from the captured result state (the prior-stage results, or the original sequences on the first stage). There is no state carried between rounds. Each proposal batch: 1. Resets proposals to the captured result state 2. Applies all generators sequentially 3. Evaluates proposals with constraints 4. Updates the sorted results list if any proposals are better than the current worst 5. Reports each proposal as its own history iteration If `energy_threshold` is set, the optimizer stops early once all best proposals have energy below the threshold. If filter constraints reject many proposals, the optimizer may return fewer than `num_results` valid results. ## How It Works Rejection sampling draws many independent proposals from the same starting point and keeps the `num_results` with the lowest energy, carrying no state between draws. Each batch resets the proposal pool to the initial sequences (the optimizer is stateless), generates `B` new proposals, scores them, and folds survivors into a buffer of the `K = num_results` lowest-energy sequences kept in sorted order: ``` keep S = the K proposals with smallest energy E insert x ⇔ |S| < K or E(x) < max(S) (bisect into sorted S, drop the worst) stop early ⇔ every s ∈ S has E(s) < energy_threshold ``` Batches repeat until `num_samples` proposals have been drawn (or the threshold is met). Because each batch restarts from the same initial pool, the search keeps no per-step state and is embarrassingly parallel. ## API Reference
ConfigRejectionSamplingOptimizerConfig Source
Configuration object for RejectionSamplingOptimizer. The Rejection Sampling optimizer generates or receives proposal sequences and keeps only the best `num_results` by lowest energy score. It processes generated proposals in internal batches and reports each proposal as the semantic iteration. If filter constraints reject many proposals (returning inf/nan energies), the optimizer may return fewer than `num_results` valid results. Generated proposal count; in existing-results mode, candidate cap. Number of top-scoring candidate designs to retain (lowest energy first). Overrides program count. Derived from the generator list when unset: generated if generators are present. Proposals scored per internal batch. Inferred from component batch sizes if omitted. Optional early-stop (lower energy = better); stops once every retained candidate is below this. Random seed for reproducible optimization, generator, and constraint tool streams. Save history and log progress every N steps. Step 0 and final step always saved. Save granular per-proposal results (accept/reject) in history snapshots. Emit per-step debug information about proposals, scores, and acceptance through the logger.
## Usage ```python python icon="python" theme={null} >>> config = RejectionSamplingOptimizerConfig(num_samples=100, num_results=10) >>> optimizer = RejectionSamplingOptimizer( ... constructs=constructs, generators=[mutation_gen], constraints=[gc_constraint], config=config ... ) >>> optimizer.run() >>> best_sequences = optimizer.constructs[0].segments[0].result_sequences ``` With early stopping: ```python python icon="python" theme={null} >>> config = RejectionSamplingOptimizerConfig( ... num_samples=1000, ... num_results=10, ... energy_threshold=0.5, # Stop when all top-10 have energy < 0.5 ... ) ``` ## Metadata | Property | Value | | ---------------------- | ---------------------------- | | Key | `rejection-sampling` | | Class | `RejectionSamplingOptimizer` | | Targets Single Segment | `False` | | Uses GPU | `False` |