{"id":"W1690153029","doi":"10.1109/cec.2004.1330836","title":"An analysis of evolutionary gradient search","year":2004,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Evolutionary computation; Evolutionary algorithm; Evolution strategy; Random search; Computer science; Function (biology); Human-based evolutionary computation; Evolutionary programming; Genetic algorithm; Gradient method; CMA-ES; Mathematical optimization; Mathematics; Artificial intelligence; Interactive evolutionary computation; Algorithm; Evolutionary biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001667482,0.0008652976,0.000888443,0.001162776,0.0005637932,0.001592505,0.000950451,0.001291726,0.005920216],"category_scores_gemma":[0.01162236,0.000408472,0.0006026122,0.001462841,0.001368519,0.002065244,0.001375801,0.001342964,0.001023138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415761,"about_ca_system_score_gemma":0.001001958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002444453,"about_ca_topic_score_gemma":0.001216549,"domain_scores_codex":[0.9990894,0.0003646227,0.00003088288,0.00009581191,0.0003467617,0.0000727142],"domain_scores_gemma":[0.998292,0.001162198,0.00011798,0.00007453665,0.0003020586,0.0000512275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003538825,0.00001904736,0.0006654132,0.0001426109,0.00005795275,0.0001057382,0.0001188365,0.2670844,0.0006140994,0.6530859,0.003407435,0.07466324],"study_design_scores_gemma":[0.00001152408,0.0000403478,0.0004888011,0.00007350198,0.00002086808,0.0001219258,0.00003115646,0.7390644,0.0003597864,0.2440127,0.01575865,0.00001630743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007966949,0.006616738,0.9261268,0.001199854,0.000192042,0.00006927312,0.00005852539,0.0001694167,0.05760026],"genre_scores_gemma":[0.6733501,0.01155304,0.2771518,0.0008122182,0.0005725347,0.0004943089,0.0002266234,0.0004669698,0.03537244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005920216,"threshold_uncertainty_score":0.01980507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02695156518853415,"score_gpt":0.3192879974002241,"score_spread":0.29233643221169,"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."}}