{"id":"W2046401452","doi":"10.1145/1389095.1389356","title":"A swarm-based crossover operator for genetic programming","year":2008,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Crossover; Symbolic regression; Genetic programming; Ant colony optimization algorithms; Computer science; Genetic algorithm; Population; Mathematical optimization; Swarm behaviour; Operator (biology); Selection (genetic algorithm); Genetic operator; Domain (mathematical analysis); Artificial intelligence; Meta-optimization; Mathematics; Machine learning; 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":[0.0006964062,0.0005023578,0.0005731171,0.0006358142,0.0005456281,0.0007915531,0.0006778315,0.0007641733,0.003096819],"category_scores_gemma":[0.001887004,0.0001997836,0.0006657144,0.001070741,0.0006610429,0.0008578978,0.000708239,0.001306502,0.000812011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000386921,"about_ca_system_score_gemma":0.0004849725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007284363,"about_ca_topic_score_gemma":0.0006803429,"domain_scores_codex":[0.999449,0.0001423635,0.0000377444,0.00007985493,0.0002632275,0.00002789613],"domain_scores_gemma":[0.9995053,0.0002197248,0.00004707058,0.00009253393,0.0001113341,0.00002414976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001886261,0.0001544041,0.001189502,0.0003872021,0.0001452969,0.0007118813,0.0005584887,0.1108005,0.06084353,0.1719567,0.008782244,0.6442817],"study_design_scores_gemma":[0.0002200044,0.0006962799,0.001510902,0.000156346,0.0002093902,0.001996987,0.00009539993,0.7013265,0.04340995,0.08396777,0.1662981,0.0001122696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004633771,0.0003532409,0.9888024,0.000174546,0.0001835322,0.00009868239,0.00004323458,0.0005390248,0.005171497],"genre_scores_gemma":[0.137644,0.0009196737,0.8481152,0.0002890259,0.0001870993,0.0003183865,0.0001711923,0.0003010285,0.01205434],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003096819,"threshold_uncertainty_score":0.01035988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267344562594191,"score_gpt":0.2614562100317404,"score_spread":0.2387827644057985,"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."}}