{"id":"W4362704594","doi":"10.4018/ijoris.321119","title":"A Computational Comparison of Three Nature-Inspired, Population-Based Metaheuristic Algorithms for Modelling-to-Generate Alternatives","year":2023,"lang":"en","type":"article","venue":"International Journal of Operations Research and Information Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Construct (python library); Set (abstract data type); Population; Computer science; Metaheuristic; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001526935,0.0001418338,0.000306266,0.0019919,0.0002826804,0.0006282089,0.0007844139,0.00008604005,0.000003139686],"category_scores_gemma":[0.0007008532,0.0001272589,0.0000816609,0.0009803864,0.00006036204,0.003194273,0.0001233342,0.0002846867,0.00001501895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001771663,"about_ca_system_score_gemma":0.000329204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008272343,"about_ca_topic_score_gemma":0.000009118256,"domain_scores_codex":[0.996704,0.0001251631,0.001214178,0.000179082,0.001551791,0.0002257787],"domain_scores_gemma":[0.9900393,0.0005878834,0.000370993,0.0001753926,0.008664119,0.0001622681],"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":[0.00005152873,0.00005875395,0.0004927038,0.00002331863,0.0000879236,0.000001568822,0.0005922298,0.9485337,0.00003024476,0.04654755,0.0001804477,0.003400072],"study_design_scores_gemma":[0.001000904,0.0002859337,0.001134944,0.00008680771,0.000004409472,0.00001648924,0.0002246599,0.9947388,0.0002907193,0.00102175,0.001078527,0.0001161165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0146083,0.0001104495,0.9823924,0.001006722,0.0009898292,0.0006377869,0.0001503335,0.00003745744,0.00006677394],"genre_scores_gemma":[0.7682512,0.00002473166,0.2312069,0.00006734487,0.0001540039,0.00007382644,0.0001765729,0.000009025463,0.00003640605],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7536429,"threshold_uncertainty_score":0.6057833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1021787136612811,"score_gpt":0.4263364513062902,"score_spread":0.324157737645009,"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."}}