{"id":"W2175876344","doi":"","title":"Improved Adaptive Genetic Algorithm in Optimal Layout of Leather Rectangular Parts","year":2015,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crossover; Rectangle; Genetic algorithm; Mass customization; Algorithm; Mathematical optimization; Convergence (economics); Key (lock); Engineering; Computer science; Personalization; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004354439,0.0004832282,0.0006166785,0.0006273997,0.0003675097,0.0005038322,0.0008096868,0.0006527702,0.001080959],"category_scores_gemma":[0.001124132,0.0002750157,0.0005495586,0.0007730835,0.0004105475,0.0005560889,0.0004172813,0.0004750005,0.0002161703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005617563,"about_ca_system_score_gemma":0.0007652525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006675426,"about_ca_topic_score_gemma":0.003895017,"domain_scores_codex":[0.9997203,0.000073943,0.00001314485,0.00006802625,0.00008695431,0.00003757168],"domain_scores_gemma":[0.9998066,0.00008209686,0.00002253734,0.00001435101,0.00006396686,0.00001041968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004417189,0.00003179095,0.0009698055,0.0000459594,0.00003645886,0.00008439309,0.0001003299,0.9159764,0.004676652,0.007733295,0.0008900254,0.06941077],"study_design_scores_gemma":[0.00001159026,0.00002124176,0.0001677344,0.00000460531,0.000008432966,0.00002580699,0.000009573662,0.9968988,0.0005789556,0.001588019,0.0006800427,0.000005184442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03550453,0.0005357944,0.9601074,0.0001236042,0.00004620213,0.00004225263,0.00001930939,0.0002497838,0.003371033],"genre_scores_gemma":[0.5955094,0.0006992354,0.3977787,0.0001122343,0.00004546561,0.0001711639,0.0001196881,0.00008014446,0.00548403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006675426,"threshold_uncertainty_score":0.01327312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075405859990016,"score_gpt":0.2735975248051858,"score_spread":0.2628434662052856,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}