{"doi":"10.1109/icbase53849.2021.00032","title":"Acquisition Functions in Bayesian Optimization","abstract":null,"journal":"2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)","year":2021,"id":646737,"datarank":0.519860385419959,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"self_citation_contribution":0.519860385419959,"citation_network_contribution":0.0,"self_endowment_contribution":0.519860385419959,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1684694,"name":"Ziyuan Ji","orcid":null,"position":1,"is_corresponding":false},{"id":1684695,"name":"Yongqing Liang","orcid":null,"position":2,"is_corresponding":false},{"id":1684693,"name":"Weiao Gan","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Acquisition Functions in Bayesian Optimization","abstract":"Bayesian optimization is effective in solving the optimization problem of black-box functions. In this work, the project focues on the optimization efficiency of three different acquisition functions (PI, EI and GP-LCB) based on the convergence speed of different test functions. At the beginning, we introduced the theorem of Bayesian optimization and gaussian process prior; later we showed the acquisition functions and benchmark functions we adopted; lastly, we carried out an experiment on the performance in different situations. In conclusion, mostly EI function is strongest but for some specific functions, PI and GP-LCB can be the more efficient one.","is_dataset_classified":null,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4210726438","authors":[],"funders":[],"total_grants":0,"fwci":3.2385,"citation_percentile":0.93594093,"influential_citations":0,"citation_trend":[{"year":2023,"count":4},{"year":2024,"count":4},{"year":2025,"count":16},{"year":2026,"count":7}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/9696011/9696012/09696089.pdf?arnumber=9696089","host_type":"publisher"},{"url":"https://doi.org/10.1109/icbase53849.2021.00032","host_type":""}],"fields_of_study":["Advanced Multi-Objective Optimization Algorithms","Metaheuristic Optimization Algorithms Research","Advanced Bandit Algorithms Research"],"mesh_terms":[],"keywords":["Bayesian optimization","Gaussian process","Benchmark (surveying)","Bayesian probability","Test functions for optimization","Convergence (economics)","Computer science","Mathematical optimization","Optimization problem","Gaussian","Function (biology)","Algorithm","Mathematics","Applied mathematics","Artificial intelligence","Multi-swarm optimization","Physics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T14:43:36.715371Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}