{"id":"W4321599248","doi":"10.1007/s43069-022-00180-6","title":"A General Mathematical Framework for Constrained Mixed-variable Blackbox Optimization Problems with Meta and Categorical Variables","year":2023,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Institut de Valorisation des Données; Hydro-Québec; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Computer science; Notation; Mathematical optimization; Optimization problem; Variable (mathematics); Continuous optimization; Context (archaeology); Flexibility (engineering); Artificial intelligence; Machine learning; Algorithm; Mathematics; Multi-swarm optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001227297,0.0002054589,0.0003095549,0.000383231,0.001004286,0.000726713,0.0004566224,0.0001395716,0.0000815658],"category_scores_gemma":[0.001141441,0.0001590106,0.00004865419,0.002275092,0.000286782,0.0009312758,0.0003327422,0.000313359,0.00002812877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008961015,"about_ca_system_score_gemma":0.0003817143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002497217,"about_ca_topic_score_gemma":0.00002121382,"domain_scores_codex":[0.9974763,0.0002178597,0.000318905,0.0006623509,0.0005715635,0.0007530208],"domain_scores_gemma":[0.9974421,0.0008191447,0.00003495538,0.0005255126,0.0009577597,0.000220491],"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":[0.000005084732,0.00005112502,0.000003966135,0.00001592947,0.00007872802,0.000003104194,0.0001472444,0.5126109,0.00005606578,0.4863826,0.0003176774,0.0003275827],"study_design_scores_gemma":[0.0005155148,0.0002064717,0.000002671257,0.0000203797,0.00002934741,0.00003677673,0.0002104737,0.9276978,0.000179607,0.07007028,0.0008418868,0.000188741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005205443,0.0000494509,0.9928799,0.004550017,0.00006631016,0.001566616,0.00003759487,0.0002531507,0.0005448931],"genre_scores_gemma":[0.004531019,0.00006228236,0.9912503,0.00008746971,0.00005370134,0.001650578,0.0000946369,0.00003755248,0.002232397],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4163123,"threshold_uncertainty_score":0.7724254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07086614826535843,"score_gpt":0.3587239731420137,"score_spread":0.2878578248766552,"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."}}