{"id":"W2743295872","doi":"10.2316/journal.206.2017.4.206-4998","title":"A MEMETIC ALGORITHM WITH VARIABLE LENGTH CHROMOSOME FOR ROBOT PATH PLANNING UNDER DYNAMIC ENVIRONMENTS","year":2017,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Memetic algorithm; Motion planning; Computer science; Path (computing); Variable (mathematics); Chromosome; Path length; Algorithm; Mathematical optimization; Artificial intelligence; Robot; Mathematics; Local search (optimization); Biology; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004082027,0.0001378398,0.0001946954,0.0001487778,0.0002369047,0.0005613427,0.0008568232,0.00005682889,0.000002085683],"category_scores_gemma":[0.00009212553,0.0001137683,0.00004531361,0.0000348391,0.00005337999,0.0009972922,0.0001284275,0.0001325852,0.000001858431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107971,"about_ca_system_score_gemma":0.00008471643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004539219,"about_ca_topic_score_gemma":1.604546e-7,"domain_scores_codex":[0.9987309,0.00002568731,0.000350378,0.0001891419,0.0005317325,0.0001721766],"domain_scores_gemma":[0.9985695,0.0001263081,0.0008128269,0.0002375278,0.0001831653,0.00007062828],"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":[0.00002167372,0.0001276253,0.0005202941,0.00001574234,0.0003287333,0.0001045168,0.0004116054,0.9394115,0.00132121,0.006601016,0.00008926785,0.05104678],"study_design_scores_gemma":[0.0009795222,0.000244451,0.01302847,0.000291391,0.00003286151,0.0003918974,0.00002754662,0.9798087,0.00009809662,0.004790624,0.0001641596,0.0001422124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002958993,0.00009335134,0.9942889,0.001579297,0.0009048046,0.0001068067,0.000007681609,0.00001921196,0.0000409284],"genre_scores_gemma":[0.2475118,0.00002625811,0.7521557,0.00007319836,0.0001192617,0.000003524155,0.000006059452,0.00001087426,0.00009331168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2445528,"threshold_uncertainty_score":0.5413041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609915289129845,"score_gpt":0.2782613534441433,"score_spread":0.2621622005528449,"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."}}