{"id":"W4392925322","doi":"10.20944/preprints202403.0898.v1","title":"Frilled Lizard Optimization: A Novel Nature-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Metaheuristic; Mathematical optimization; Computer science; Optimization algorithm; Algorithm; 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":[0.0008399316,0.0008504867,0.0008049016,0.0009121369,0.0003958207,0.0008042277,0.00148676,0.001423366,0.00141069],"category_scores_gemma":[0.001662874,0.0003565688,0.000992242,0.000721349,0.000530331,0.0006056961,0.0008787329,0.000901396,0.0003346973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007337047,"about_ca_system_score_gemma":0.001097214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004429337,"about_ca_topic_score_gemma":0.004788379,"domain_scores_codex":[0.9996513,0.0001389681,0.00001786411,0.00005374928,0.0001023217,0.00003568097],"domain_scores_gemma":[0.9997285,0.0001487382,0.00003786367,0.00002550612,0.00004182801,0.00001754125],"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":[0.00006885723,0.00007749951,0.001411176,0.0001344238,0.0001580067,0.00008737236,0.0000598831,0.8918287,0.003256985,0.01212828,0.00188958,0.08889934],"study_design_scores_gemma":[0.0000169677,0.0000417974,0.0001476048,0.00001161353,0.00001434655,0.0000307142,0.000006450847,0.995703,0.0005031002,0.001331215,0.002188097,0.000004999177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0327061,0.001586024,0.9564632,0.0003490663,0.0001143379,0.0001511166,0.00008421234,0.0006515041,0.007894376],"genre_scores_gemma":[0.3046932,0.0008107927,0.6885488,0.0004281263,0.0001027191,0.0005040711,0.000264383,0.0002110661,0.004436862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004429337,"threshold_uncertainty_score":0.008807123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058456975479414,"score_gpt":0.3614883443070006,"score_spread":0.2556426467590592,"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."}}