{"id":"W2559710837","doi":"10.1109/cec.2016.7744349","title":"Heritage-dynamic cultural algorithm for multi-population solutions","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Minnesota Pollution Control Agency","keywords":"Population; Set (abstract data type); Computer science; Limit (mathematics); Genetic algorithm; Cultural algorithm; Algorithm; Cultural heritage; Space (punctuation); Artificial intelligence; Mathematical optimization; Machine learning; Population-based incremental learning; Mathematics; Sociology; Geography; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000362409,0.0001158332,0.0001264038,0.0001157906,0.0002404776,0.0001557412,0.0005585546,0.00005819856,0.0001127461],"category_scores_gemma":[0.0002523647,0.00007263833,0.00007626975,0.0002969209,0.0000452097,0.000778852,0.0001892242,0.00004728332,0.0001383163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001063729,"about_ca_system_score_gemma":0.00004381703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000262005,"about_ca_topic_score_gemma":0.00002252636,"domain_scores_codex":[0.9986163,0.00007110315,0.0002464983,0.0003819044,0.0002850624,0.0003991703],"domain_scores_gemma":[0.9989203,0.0001524605,0.00005599845,0.0004103575,0.0003323343,0.0001285509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001458055,0.00008104656,0.00005396746,0.00000584669,0.00001887127,0.000001865001,0.00005890297,0.0001069314,0.0005717233,0.02454889,0.001450973,0.9730995],"study_design_scores_gemma":[0.0007019587,0.00003473172,0.001301846,0.000007251113,0.000002800925,0.000007677841,0.00001256646,0.9954912,0.0001082352,0.0008033867,0.001387441,0.0001409212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000603767,0.00003524797,0.9970749,0.001579076,0.0003280376,0.0004139663,0.00002185423,0.000272689,0.0002138673],"genre_scores_gemma":[0.01087935,0.00002855193,0.9760899,0.00006804328,0.00004240343,0.0000963457,0.00001530939,0.00001144897,0.01276865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9953843,"threshold_uncertainty_score":0.2962105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07195878933908625,"score_gpt":0.34952968208199,"score_spread":0.2775708927429037,"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."}}