{"id":"W2947748448","doi":"10.3390/info10060184","title":"FPGA Implementation of Crossover Module of Genetic Algorithm","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Field-programmable gate array; Computer science; Travelling salesman problem; Genetic algorithm; Parallel computing; Software; Realization (probability); Computer architecture; Field (mathematics); Algorithm; Embedded system; Operating system; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001079473,0.000242167,0.0001867144,0.0003959319,0.0001752086,0.0003262239,0.0005171625,0.0002381377,0.003121318],"category_scores_gemma":[0.0002574577,0.0001076953,0.0001534716,0.0002754863,0.0001003386,0.0002049221,0.0001062978,0.0002661026,0.0005334032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000224126,"about_ca_system_score_gemma":0.0002779221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112903,"about_ca_topic_score_gemma":0.0008233939,"domain_scores_codex":[0.999879,0.00002168174,0.000005781942,0.00002134785,0.00004736426,0.00002477476],"domain_scores_gemma":[0.9999217,0.00001972988,0.00001056893,0.00001418409,0.0000280075,0.000005775586],"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":[0.000719196,0.000139525,0.002860669,0.0004623357,0.0001087326,0.0009403619,0.0001946097,0.06463028,0.2849055,0.02036954,0.007790464,0.6168789],"study_design_scores_gemma":[0.0003669438,0.00168379,0.008860919,0.0001177706,0.0001429069,0.003415385,0.00007484886,0.4363964,0.4633735,0.004106468,0.08137475,0.00008637807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1453885,0.001397047,0.8149683,0.0003259938,0.0004475647,0.0001820434,0.0002097704,0.008085169,0.02899566],"genre_scores_gemma":[0.7948558,0.0004881472,0.1970721,0.0001111873,0.00007177512,0.00007184415,0.0002031249,0.00008063401,0.007045534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003121318,"threshold_uncertainty_score":0.01044184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009378328181832317,"score_gpt":0.2873228973729056,"score_spread":0.2779445691910732,"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."}}