{"id":"W4396920847","doi":"10.31857/s0005117924030037","title":"Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems","year":2024,"lang":"en","type":"article","venue":"Automation and Remote Control","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Crossover; Metaheuristic; Benchmark (surveying); Genetic algorithm; Computer science; Combinatorial optimization; Algorithm; Mathematical optimization; Convergence (economics); Heuristic; Randomness; Exploit; Quality control and genetic algorithms; Premature convergence; Meta-optimization; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008783487,0.001560333,0.00151909,0.001388238,0.00066509,0.001082816,0.001474389,0.001911728,0.001679014],"category_scores_gemma":[0.001820036,0.0004206345,0.001443903,0.002290928,0.000858599,0.001019214,0.0009617831,0.001782935,0.0005939323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007596452,"about_ca_system_score_gemma":0.00175622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003753225,"about_ca_topic_score_gemma":0.003501436,"domain_scores_codex":[0.9993641,0.0002283865,0.00003798558,0.00008751407,0.0002378829,0.00004415436],"domain_scores_gemma":[0.9996557,0.0001999758,0.00004927448,0.00003043142,0.0000512632,0.0000134002],"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":[0.00004442454,0.0001018816,0.0008016418,0.0003491965,0.000201036,0.0001769213,0.00007939934,0.7675742,0.004525117,0.0362805,0.004829193,0.1850365],"study_design_scores_gemma":[0.00005997049,0.00009037674,0.0002015587,0.0000797445,0.00006248386,0.0001758631,0.00003047771,0.9554778,0.002571729,0.0215356,0.01968789,0.00002650898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00622686,0.002521809,0.9833771,0.0003742015,0.0001280619,0.0001523292,0.00008629652,0.0006103473,0.006523036],"genre_scores_gemma":[0.1167184,0.003455551,0.874413,0.0003151801,0.0001121313,0.0005966035,0.0003620706,0.0002048905,0.003822344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003753225,"threshold_uncertainty_score":0.00746274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009434068072688354,"score_gpt":0.2482859273020918,"score_spread":0.2388518592294034,"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."}}