{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001431299,0.0003384093,0.000455203,0.0008094531,0.0001180401,0.0000879067,0.001147076,0.0001073725,0.0000150254],"category_scores_gemma":[0.0002912869,0.0002707407,0.00005939169,0.004806128,0.001749144,0.004215716,0.0001440876,0.0005707167,0.000004426556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004030848,"about_ca_system_score_gemma":0.0002231491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001100501,"about_ca_topic_score_gemma":0.00115071,"domain_scores_codex":[0.9966213,0.00007983841,0.0006967228,0.000733031,0.000913748,0.000955343],"domain_scores_gemma":[0.9991152,0.0001668221,0.0001730364,0.0002388934,0.0001546027,0.000151465],"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":[0.00002964772,0.00003402295,0.006103642,0.0000213731,0.000001856621,0.0000128544,0.0009106742,0.8605949,0.0007014078,0.0004506318,0.000005322799,0.1311336],"study_design_scores_gemma":[0.001063342,0.0002515099,0.003131532,0.0002437784,0.000003552642,0.00001860063,0.001465419,0.9834558,0.004176714,0.003329846,0.002224526,0.0006353752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5553477,0.4072637,0.005987134,0.000244955,0.01190026,0.001848461,0.00002709471,0.0003887608,0.0169919],"genre_scores_gemma":[0.899462,0.002619224,0.09772889,0.00003846518,0.00005823268,0.00002786017,0.000002307544,0.00001475353,0.00004823211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4046445,"threshold_uncertainty_score":0.9999745,"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."}}