{"id":"W3171336700","doi":"10.1007/s00366-021-01409-4","title":"An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm","year":2021,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Slime Mold and Myxomycetes Research","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Chaotic; Slime mold; Benchmark (surveying); Algorithm; SMA*; Computer science; Mathematical optimization; Maxima and minima; Convergence (economics); Heuristic; 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":[0.0003542463,0.0004257526,0.0006322481,0.0003771405,0.0003850218,0.0004435885,0.0005652797,0.0009117755,0.00168781],"category_scores_gemma":[0.001174805,0.0002853342,0.0005371409,0.0003788179,0.000538713,0.0004177464,0.0007385708,0.0005285051,0.0002050409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002924421,"about_ca_system_score_gemma":0.0008585091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002179594,"about_ca_topic_score_gemma":0.002110064,"domain_scores_codex":[0.9998695,0.00003868953,0.000007401291,0.00001599902,0.00005459392,0.00001390393],"domain_scores_gemma":[0.9997053,0.0001611525,0.00003117138,0.00002115846,0.00006394446,0.00001721083],"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":[0.00005616915,0.00003624938,0.0004629249,0.0000763955,0.00002839069,0.00006101455,0.00004764505,0.9515323,0.00471139,0.01267969,0.0005751255,0.02973274],"study_design_scores_gemma":[0.000008820606,0.00001689407,0.00003740881,0.000002420731,0.000002569441,0.000006156014,0.000003499986,0.9981775,0.0003578275,0.001004984,0.0003801511,0.000001763622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04484342,0.0002072195,0.9484586,0.0001652468,0.00008086671,0.0000558317,0.00002708752,0.0001639807,0.005997729],"genre_scores_gemma":[0.5142347,0.0002029315,0.48038,0.0001010495,0.00003724551,0.0002556173,0.00006504906,0.00007965153,0.004643771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002179594,"threshold_uncertainty_score":0.005646348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01774659696235804,"score_gpt":0.2414251042960875,"score_spread":0.2236785073337295,"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."}}