{"id":"W1770820624","doi":"10.1109/cec.2004.1330933","title":"Enhancement of the shifting balance genetic algorithm for highly multimodal problems","year":2004,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Knapsack problem; Genetic algorithm; Computer science; Mathematical optimization; Extension (predicate logic); Population; Balance (ability); Continuous knapsack problem; Function (biology); Mechanism (biology); Algorithm; Machine learning; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003744352,0.0001093383,0.0001473398,0.00005736333,0.0001270144,0.00007563535,0.001130533,0.00003667049,0.00002774467],"category_scores_gemma":[0.0001135686,0.00007416298,0.00007183669,0.0004420486,0.00006425244,0.0001339426,0.0003102241,0.00008022418,0.00001376566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000586415,"about_ca_system_score_gemma":0.0001773307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000568964,"about_ca_topic_score_gemma":0.000003014936,"domain_scores_codex":[0.9984596,0.00004634908,0.0003468062,0.0003314289,0.0005051583,0.0003106395],"domain_scores_gemma":[0.9988621,0.0001001982,0.0001296106,0.0005775536,0.0002632608,0.00006725642],"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":[0.00000623282,0.0007310357,0.0003926812,0.0002126385,0.0001067796,0.000004014577,0.001643337,0.1720147,0.007775684,0.05342478,0.0004681486,0.76322],"study_design_scores_gemma":[0.0007687247,0.00008057427,0.0005367542,0.00002539098,0.000003224388,0.00000250139,0.000006673757,0.9587979,0.03671919,0.002521729,0.0004357014,0.0001016323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007594908,0.00006337119,0.9964939,0.00105589,0.0002769803,0.0007902491,0.000004519839,0.00004668036,0.0005088915],"genre_scores_gemma":[0.04530566,0.00001522967,0.9535801,0.0001251173,0.00004229623,0.0001016811,0.000001155898,0.000009120084,0.0008196568],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7867832,"threshold_uncertainty_score":0.3024279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811963696650311,"score_gpt":0.2675211502253758,"score_spread":0.2494015132588726,"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."}}