{"id":"W2734764825","doi":"10.1007/978-3-319-54978-1_86","title":"A Multiobjective Optimization Method for the SOC Test Time, TAM, and Power Optimization Using a Strength Pareto Evolutionary Algorithm","year":2017,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pareto principle; Multi-objective optimization; Evolutionary algorithm; Mathematical optimization; Computer science; Pareto optimal; Minification; Scheduling (production processes); Power (physics); Job shop scheduling; System on a chip; Algorithm; Mathematics; Embedded system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007204657,0.0003641839,0.0004728581,0.000178284,0.0008326821,0.0003970332,0.0004404368,0.0001836133,0.000002605522],"category_scores_gemma":[0.0002462985,0.0003069967,0.00008238106,0.0000570279,0.0001071125,0.0005499572,0.0002913079,0.0002644277,9.151409e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001372789,"about_ca_system_score_gemma":0.00008043143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007979165,"about_ca_topic_score_gemma":0.000005469747,"domain_scores_codex":[0.9980453,0.00006282326,0.0005991849,0.0007358926,0.000247447,0.0003093974],"domain_scores_gemma":[0.9966493,0.001813758,0.0008033057,0.0003856275,0.0002806571,0.00006731119],"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":[9.36499e-7,0.00001225163,0.00006708453,0.00007624637,0.0000332766,0.000004799815,0.0003414347,0.7616825,0.000001845415,0.00603026,0.000007305996,0.2317421],"study_design_scores_gemma":[0.0001805562,0.00007891374,0.00001189058,0.00122474,0.00003534784,0.00009077226,0.0001241955,0.9955345,0.000004428909,0.0007893431,0.001572995,0.0003522861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000002364272,0.01409671,0.981348,0.0000249155,0.0005821426,0.0008121146,0.00003014391,0.00007212217,0.003031449],"genre_scores_gemma":[0.02005646,0.002875579,0.9711412,0.00009180391,0.0007100973,0.00005743838,0.00004805303,0.0001034743,0.004915937],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2338521,"threshold_uncertainty_score":0.9999382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140967196367243,"score_gpt":0.2920815301207565,"score_spread":0.2706718581570841,"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."}}