{"id":"W4226322432","doi":"10.1088/1742-6596/2216/1/012035","title":"An Improved Genetic Algorithm for Rapid UAV Path Planning","year":2022,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Motion planning; Robustness (evolution); Fitness function; Genetic algorithm; Computer science; Obstacle; Swarm behaviour; Path (computing); Mathematical optimization; Algorithm; Real-time computing; Artificial intelligence; Mathematics; Robot; Machine learning; Geography","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":[0.0006317376,0.0008022602,0.0008584564,0.001007742,0.0005430492,0.0005646067,0.001008194,0.001089395,0.001498884],"category_scores_gemma":[0.00120446,0.0003719233,0.0006497466,0.0009624422,0.0005528648,0.0005172293,0.0006305276,0.0007294442,0.000249272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000912432,"about_ca_system_score_gemma":0.001451882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01178884,"about_ca_topic_score_gemma":0.005090631,"domain_scores_codex":[0.9996037,0.0001020436,0.0000193367,0.00007412139,0.0001529499,0.00004785687],"domain_scores_gemma":[0.9996678,0.0001500378,0.00003051327,0.00001919744,0.0001169916,0.00001548276],"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":[0.00002781204,0.00002652864,0.0003218949,0.00002912809,0.00002267084,0.00006603018,0.00004445915,0.9505847,0.002758117,0.004272324,0.0006812735,0.04116505],"study_design_scores_gemma":[0.00001224976,0.00001544738,0.00006057578,0.000003266224,0.000005405639,0.00001737696,0.000003317843,0.9985793,0.00032643,0.0005129647,0.0004599473,0.000003774594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02961199,0.0003630582,0.965855,0.0001705122,0.0000793174,0.00008435849,0.00003934075,0.0004604093,0.003335989],"genre_scores_gemma":[0.5052655,0.0004025369,0.4887085,0.0001285747,0.00005427224,0.000383602,0.0001879282,0.000101149,0.004767839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01178884,"threshold_uncertainty_score":0.02344048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02950787857700511,"score_gpt":0.2696342385371157,"score_spread":0.2401263599601106,"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."}}