{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001818546,0.0002000233,0.0001873688,0.0001816678,0.0005906993,0.0001679107,0.0004820151,0.00006434626,0.000003458831],"category_scores_gemma":[0.00003538505,0.0001999303,0.00005274417,0.001105538,0.00008545662,0.0002923289,0.000504892,0.0002223908,0.00009935296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004357972,"about_ca_system_score_gemma":0.00002857346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001432429,"about_ca_topic_score_gemma":0.000001509122,"domain_scores_codex":[0.9984136,0.0000627293,0.00025315,0.0006983094,0.0002150841,0.0003571286],"domain_scores_gemma":[0.9988596,0.000240669,0.0001231012,0.0004524297,0.0001845713,0.0001396597],"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":[0.000002013834,0.0001167325,0.0002055321,0.00001252758,0.00002575642,0.00001331849,0.0006955279,0.251561,0.0005572611,0.003604194,0.0001598572,0.7430463],"study_design_scores_gemma":[0.0005292912,0.00002154875,0.003013341,0.000007023358,0.000005233684,0.00003253519,0.0001022052,0.9944696,0.0003159549,0.0005726212,0.0007032728,0.0002273604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008666855,0.00007287213,0.9965259,0.0004358122,0.0001787226,0.0005128531,0.00001401949,0.00114216,0.0002509806],"genre_scores_gemma":[0.1113737,0.0000540707,0.8870656,0.0002980744,0.0001832831,0.000179323,0.00002942468,0.00003077774,0.0007857722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7429087,"threshold_uncertainty_score":0.8152921,"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."}}