{"id":"W4294811616","doi":"10.1109/cec55065.2022.9870353","title":"Managing Diversity and Many Objectives in Evolutionary Design","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Congress on Evolutionary Computation (CEC)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Evolutionary algorithm; Image (mathematics); Diversity (politics); Quality (philosophy); Genetic programming; Artificial intelligence; Road map; Evolutionary computation; Mathematical optimization; Theoretical computer science; Mathematics; Geography; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002843713,0.000904704,0.0008979608,0.001246008,0.0009082521,0.001646794,0.001274437,0.00118625,0.001991359],"category_scores_gemma":[0.005250442,0.000595297,0.0009420189,0.000916212,0.001561649,0.002349555,0.002615553,0.002005943,0.0003187532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009997688,"about_ca_system_score_gemma":0.0008536393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009346415,"about_ca_topic_score_gemma":0.001260052,"domain_scores_codex":[0.9982479,0.0007162818,0.00008817171,0.0001539199,0.0006822123,0.0001115946],"domain_scores_gemma":[0.9981291,0.001117073,0.0001682354,0.0002354665,0.0002619989,0.00008817786],"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":[0.00009468784,0.0001080319,0.001789942,0.0002548191,0.000183815,0.000209386,0.0005679429,0.5873592,0.009084383,0.1297974,0.001503074,0.2690473],"study_design_scores_gemma":[0.00006673886,0.0003074177,0.000483229,0.0001006834,0.00009156836,0.0003559913,0.0001069578,0.8364202,0.004456092,0.1415127,0.01604314,0.00005535584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01378722,0.0007848298,0.9802796,0.0002966849,0.00004139892,0.00004063147,0.00001013992,0.0001572174,0.004602299],"genre_scores_gemma":[0.3663628,0.001190954,0.6258115,0.000289399,0.0001646515,0.0003003161,0.00005156028,0.0001593405,0.005669371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002843713,"threshold_uncertainty_score":0.01503915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02340956873147168,"score_gpt":0.2472991015678859,"score_spread":0.2238895328364142,"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."}}