{"id":"W2504494579","doi":"10.1145/2908812.2908887","title":"Discovering Rubik's Cube Subgroups using Coevolutionary GP","year":2016,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reuse; Task (project management); Computer science; Genetic programming; Reinforcement learning; Cube (algebra); Population; Decomposition; Process (computing); Genetic algorithm; Sequence (biology); Theoretical computer science; Artificial intelligence; Machine learning; Mathematics; Programming language; Engineering","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":[0.001383446,0.0006776568,0.001128706,0.0008153147,0.0007895398,0.001141371,0.001456609,0.001128131,0.002381425],"category_scores_gemma":[0.003978933,0.0005502349,0.001635905,0.0006339686,0.001314931,0.001782977,0.002081236,0.001618752,0.0003479347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001209466,"about_ca_system_score_gemma":0.001940638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00665096,"about_ca_topic_score_gemma":0.005804791,"domain_scores_codex":[0.9994362,0.0002021315,0.00002835091,0.0001294793,0.0001115942,0.00009217981],"domain_scores_gemma":[0.9987691,0.0007362919,0.0001065105,0.0001921348,0.0001140286,0.00008181699],"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":[0.0001148778,0.0001312849,0.003266157,0.00009620188,0.00009221466,0.0002297383,0.0004364909,0.8306705,0.002816659,0.07744622,0.001867551,0.08283199],"study_design_scores_gemma":[0.00002250304,0.00004652935,0.0001509237,0.000009966816,0.00001642763,0.00002721817,0.00008650477,0.9631559,0.0007206383,0.03451096,0.00124337,0.000009067286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1941059,0.0002294413,0.7935936,0.0005858016,0.00003533283,0.0002489519,0.0001256735,0.0005654202,0.01050983],"genre_scores_gemma":[0.5869198,0.0001983491,0.4080837,0.0002294047,0.00001981563,0.0003715919,0.0003029931,0.0001507346,0.003723642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00665096,"threshold_uncertainty_score":0.01322448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973406502928331,"score_gpt":0.2488907133229868,"score_spread":0.2291566482937035,"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."}}