{"id":"W2123251383","doi":"10.1162/evco.2007.15.2.199","title":"Reducing the Number of Fitness Evaluations in Graph Genetic Programming Using a Canonical Graph Indexed Database","year":2007,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic programming; Computer science; Graph; Graph database; Theoretical computer science; Artificial intelligence","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":[0.001742172,0.0004628611,0.0007523843,0.0006462418,0.0004153575,0.001450323,0.001460562,0.0009401297,0.00263504],"category_scores_gemma":[0.008380109,0.0003194216,0.0004381003,0.001154183,0.001025846,0.003160293,0.001617103,0.001137398,0.0004462771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008509139,"about_ca_system_score_gemma":0.001450036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004017024,"about_ca_topic_score_gemma":0.004009758,"domain_scores_codex":[0.9989873,0.0004390924,0.00004793348,0.0001869157,0.000253594,0.00008523596],"domain_scores_gemma":[0.9970861,0.001889845,0.0001312474,0.0005120051,0.0002899347,0.00009094081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001888924,0.0001968945,0.003965044,0.0001150388,0.00004700075,0.0002044987,0.0002524553,0.6167067,0.006852253,0.1030324,0.00178356,0.2666553],"study_design_scores_gemma":[0.00003569069,0.00008604168,0.0004451045,0.00001083482,0.00003468536,0.00008239576,0.00008658528,0.9372287,0.004432701,0.0555523,0.001989244,0.00001573119],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.163701,0.0002276009,0.8292408,0.0004439365,0.00004777718,0.00009744071,0.00007788241,0.001431982,0.004731662],"genre_scores_gemma":[0.598004,0.0003124228,0.3984607,0.0001499975,0.00001994663,0.0001884715,0.0002236772,0.0002814156,0.00235939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004017024,"threshold_uncertainty_score":0.009213567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03178961716159892,"score_gpt":0.339221223998637,"score_spread":0.3074316068370381,"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."}}