Release algorithm evolution selector v0.1

LICENSE

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+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+Copyright 2026 Douvras Labs.

README.md

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+---
+license: cc-by-4.0
+tags:
+- synthetic
+- optimization
+- algorithm-selection
+---
+
+# Douvras Algorithm Evolution Selector v0.1
+
+Transparent baseline that rejects incorrect candidates and minimizes the
+synthetic latency-memory objective among valid candidates. It has no neural
+weights and requires real benchmarking before operational use.

metrics.json

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+{
+  "test": {
+    "invalid_candidates_rejected": true,
+    "top1_valid_selection": 1.0,
+    "workloads": 2
+  },
+  "train": {
+    "invalid_candidates_rejected": true,
+    "top1_valid_selection": 1.0,
+    "workloads": 8
+  },
+  "validation": {
+    "invalid_candidates_rejected": true,
+    "top1_valid_selection": 1.0,
+    "workloads": 2
+  }
+}

model.json

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+{
+  "algorithm": "deterministic correctness gate plus latency-memory objective",
+  "human_review_required": true,
+  "metrics": {
+    "test": {
+      "invalid_candidates_rejected": true,
+      "top1_valid_selection": 1.0,
+      "workloads": 2
+    },
+    "train": {
+      "invalid_candidates_rejected": true,
+      "top1_valid_selection": 1.0,
+      "workloads": 8
+    },
+    "validation": {
+      "invalid_candidates_rejected": true,
+      "top1_valid_selection": 1.0,
+      "workloads": 2
+    }
+  },
+  "model_type": "algorithm_evolution_selector",
+  "neural_weights": false,
+  "real_benchmark_required": true,
+  "synthetic_only": true,
+  "training_data": "douvras-algorithm-evolution-benchmark train split only"
+}

predictor.py

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+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+
+def select(rows: list[dict]) -> dict:
+    valid = [r for r in rows if r["correctness_pass"]]
+    chosen = min(valid, key=lambda r: (r["latency_ms"] + 0.1 * r["memory_mb"], r["candidate_id"]))
+    return {"candidate_id": chosen["candidate_id"], "objective_score": round(chosen["latency_ms"] + 0.1 * chosen["memory_mb"], 6), "human_review_required": True}
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("records_json", type=Path)
+    args = parser.parse_args()
+    print(json.dumps(select(json.loads(args.records_json.read_text(encoding="utf-8"))), ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()

provenance.json

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+{
+  "model": "dougdotcon/douvras-algorithm-evolution-selector",
+  "version": "0.1.0",
+  "algorithm": "deterministic correctness gate plus latency-memory objective",
+  "training_dataset": "dougdotcon/douvras-algorithm-evolution-benchmark",
+  "synthetic_only": true,
+  "neural_weights": false,
+  "real_benchmark_required": true,
+  "limitations": ["synthetic metrics", "no hardware benchmark", "human review required"]
+}