{"slug":"jaechang-hits-pymoo","source_name":"jaechang-hits/pymoo","name":"Jaechang Hits/Pymoo","description":"Python framework for single- and multi-objective optimization with evolutionary algorithms. Define vectorized objectives and constraints; solve with NSGA-II, NSGA-III, MOEA/D, GAs, or differential evolution. Analyze Pareto fronts, visualize trade-offs, customize operators and callbacks. For engineering design, hyperparameter search, and conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient), platypus, jMetalPy (Java).","version":1,"lift":{"pass_rate_delta_pts":30.43,"pass_rate_pct":100,"total_cases":23,"passed_cases":23,"tokens_delta_pct":228.6,"turns_delta_pct":0,"verdict":"pass","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-24T11:33:41.733565+00:00"},"skill_score":1,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":30.43,"with_pass_pct":100,"without_pass_pct":69.6,"tokens_delta_pct":228.6,"turns_delta_pct":0,"total_cases":23,"cases_aggregated":23,"verdict":"pass","never_hurt":true,"completed_at":"2026-08-24T11:33:41.733565+00:00","run_id":"29f49185-d302-442f-8fe2-2b3a13491883","version_number":1,"is_latest_version":true,"gate":null}],"trust":{"skill_safety":"passed","safety_status":"clean","intent_verdict":"safe","content_status":"clean","indexable":true},"license":"Apache-2.0","install_count":0,"manifest_hash":"f11ac00c65e08f13f7901e74594cb7b7a5253850386eede3fb873ea1b79942c4","raw_url":"https://app.decimal.ai/s/jaechang-hits-pymoo/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/jaechang-hits-pymoo"}