{"id":"W4321435904","doi":"10.1007/s00521-023-08332-3","title":"Multi-objective fitness-dependent optimizer algorithm","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fanshawe College","funders":"","keywords":"Computer science; Benchmark (surveying); Sorting; Evolutionary algorithm; Mathematical optimization; Test suite; Particle swarm optimization; Algorithm; Genetic algorithm; Fitness function; Variety (cybernetics); Domain (mathematical analysis); Computation; Test case; Machine learning; Artificial intelligence; Mathematics","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":[0.001598223,0.001042632,0.001353194,0.0008185262,0.0005042871,0.0008280855,0.001802181,0.001753323,0.004951843],"category_scores_gemma":[0.002585202,0.0005074392,0.0008995154,0.000792674,0.0004231934,0.0007082401,0.001169788,0.001223206,0.001277206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005785649,"about_ca_system_score_gemma":0.001000136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001620662,"about_ca_topic_score_gemma":0.001831577,"domain_scores_codex":[0.9993231,0.0002213497,0.00003656095,0.0001331235,0.0002304141,0.0000554215],"domain_scores_gemma":[0.9992796,0.0002700917,0.00007067345,0.00008889945,0.0002543772,0.00003655444],"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":[0.0001649138,0.0001335517,0.000819999,0.0001285509,0.0001526044,0.00009724903,0.00004353358,0.7961457,0.004651733,0.00915498,0.003828346,0.1846788],"study_design_scores_gemma":[0.00001710529,0.00003725198,0.0001801736,0.000006607764,0.00001247103,0.00001948564,0.000002374072,0.9979546,0.0006241515,0.0006154003,0.0005258868,0.000004421835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01194451,0.0003276246,0.9818745,0.0001463326,0.00008595758,0.00008370645,0.00006512926,0.000540189,0.004932216],"genre_scores_gemma":[0.2832355,0.0002053672,0.7061498,0.0002703071,0.00007729678,0.0004228095,0.000351149,0.0002345626,0.009053136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004951843,"threshold_uncertainty_score":0.01656556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02226722897882599,"score_gpt":0.3027546914269665,"score_spread":0.2804874624481405,"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."}}