{"id":"W2618467698","doi":"10.1007/s12293-017-0234-5","title":"A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function","year":2017,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Premature convergence; Crossover; Maxima and minima; Algorithm; Population; Genetic algorithm; Population-based incremental learning; Mathematical optimization; Computer science; Benchmark (surveying); Curse of dimensionality; Operator (biology); Convergence (economics); Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006238335,0.00060274,0.00117167,0.0008007289,0.0004368832,0.0007489477,0.001277591,0.001739429,0.002594984],"category_scores_gemma":[0.000769774,0.0003733227,0.0007817206,0.0009138841,0.0004460704,0.0007444198,0.0007192286,0.0007228454,0.0006513545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004491221,"about_ca_system_score_gemma":0.0006347676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002236996,"about_ca_topic_score_gemma":0.002229369,"domain_scores_codex":[0.9997055,0.00007125396,0.00001175253,0.00003654836,0.0001518161,0.00002309279],"domain_scores_gemma":[0.9998627,0.00005714991,0.00001178908,0.00001675751,0.00004288122,0.000008736868],"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":[0.0001359743,0.00009939056,0.000472845,0.0001823371,0.000164277,0.0001896523,0.00006350255,0.8098974,0.0158872,0.02121896,0.003005912,0.1486825],"study_design_scores_gemma":[0.00001716154,0.0000372302,0.00009028826,0.000006357017,0.00001716942,0.00003299624,0.00000377957,0.9961553,0.001100622,0.001364262,0.001168408,0.000006324195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01553629,0.0007802204,0.975054,0.0001836572,0.0001278815,0.00007419106,0.00003514773,0.0004790617,0.007729596],"genre_scores_gemma":[0.3533109,0.0005869452,0.6331524,0.0002361009,0.0001128508,0.0002751616,0.0001046706,0.0001933374,0.01202762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002594984,"threshold_uncertainty_score":0.008681059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0210543000855937,"score_gpt":0.2711286795990178,"score_spread":0.2500743795134241,"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."}}