{"id":"W94609200","doi":"10.1007/978-0-387-87623-8_4","title":"Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study","year":2008,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Benchmarking; Genetic programming; Benchmark (surveying); Computer science; Decomposition; Mathematical optimization; Domain (mathematical analysis); Pareto principle; Coevolution; Genetic algorithm; Operator (biology); Artificial intelligence; Multi-objective optimization; Machine learning; Mathematics; Economics; Ecology; Chemistry; Biology","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":[0.003995539,0.0006955111,0.00102667,0.002000408,0.0009422662,0.002660575,0.002472734,0.001509692,0.002418208],"category_scores_gemma":[0.01411367,0.0001571888,0.0004502149,0.004168253,0.001017846,0.002122031,0.00101284,0.001223458,0.000426518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001756742,"about_ca_system_score_gemma":0.001196778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006762089,"about_ca_topic_score_gemma":0.00471038,"domain_scores_codex":[0.9978618,0.0008200975,0.00005496418,0.0002396596,0.0008287603,0.0001946847],"domain_scores_gemma":[0.9937774,0.003773909,0.0002430188,0.0005773719,0.001431459,0.0001969735],"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":[0.0006618465,0.0007309775,0.008267939,0.0002787205,0.0001311077,0.0001386338,0.0003803366,0.4841617,0.001469475,0.1823413,0.01355808,0.3078798],"study_design_scores_gemma":[0.00001640538,0.0001123944,0.001182336,0.0000192216,0.00001802366,0.00004675455,0.0001199931,0.9736879,0.0008290877,0.02240922,0.001549769,0.000008890212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4985019,0.005517564,0.3920836,0.001378946,0.0002503478,0.0002889178,0.0004693802,0.0004806228,0.1010287],"genre_scores_gemma":[0.9272451,0.0008620569,0.06247433,0.0001296506,0.00006231595,0.00008930486,0.0004623579,0.0001120055,0.008562901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006762089,"threshold_uncertainty_score":0.02113068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02575425611904595,"score_gpt":0.2476444151773596,"score_spread":0.2218901590583136,"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."}}