{"id":"https://openalex.org/W7160944046","doi":"https://doi.org/10.48550/arxiv.2605.09658","title":"Zoom, Don't Wander: Why Regional Search Outperforms Pareto Reasoning and Global Optimization in Budget-Constrained SBSE","display_name":"Zoom, Don't Wander: Why Regional Search Outperforms Pareto Reasoning and Global Optimization in Budget-Constrained SBSE","publication_year":2026,"publication_date":"2026-05-10","ids":{"openalex":"https://openalex.org/W7160944046","doi":"https://doi.org/10.48550/arxiv.2605.09658"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09658","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09658","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.09658","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135974904","display_name":"Kishan Kumar Ganguly","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ganguly, Kishan Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135988539","display_name":"Tim Menzies","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Menzies, Tim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.490200012922287,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.490200012922287,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.06539999693632126,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.04399999976158142,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/global-optimization","display_name":"Global optimization","score":0.6348000168800354},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.6261000037193298},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.5307999849319458},{"id":"https://openalex.org/keywords/tying","display_name":"Tying","score":0.4611999988555908},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.45669999718666077},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4546000063419342},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.4399999976158142},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.3912000060081482}],"concepts":[{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.6348000168800354},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.6261000037193298},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5273000001907349},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.47209998965263367},{"id":"https://openalex.org/C2780938662","wikidata":"https://www.wikidata.org/wiki/Q973710","display_name":"Tying","level":2,"score":0.4611999988555908},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.45669999718666077},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.3912000060081482},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3662000000476837},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34619998931884766},{"id":"https://openalex.org/C2778599509","wikidata":"https://www.wikidata.org/wiki/Q36829","display_name":"Pareto efficiency","level":3,"score":0.34439998865127563},{"id":"https://openalex.org/C124913957","wikidata":"https://www.wikidata.org/wiki/Q1232548","display_name":"Zoom","level":3,"score":0.322299987077713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3034000098705292},{"id":"https://openalex.org/C139143892","wikidata":"https://www.wikidata.org/wiki/Q7441615","display_name":"Search-based software engineering","level":5,"score":0.2955000102519989},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.28139999508857727},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2556999921798706},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09658","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09658","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.09658","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09658","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4025413691997528,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"Search-Based":[1],"Software":[2,27],"Engineering":[3,28],"(SBSE)":[4],"assumes":[5],"global":[6,43,64,170],"search":[7],"and":[8,42,63,71,182,191],"full":[9],"Pareto":[10,41,62,107],"exploration":[11,44],"are":[12],"essential.":[13],"We":[14],"offer":[15],"the":[16,85,128],"following":[17],"negative":[18],"result":[19],"based":[20],"on":[21,79,109],"a":[22,101,134,140,186],"study":[23],"of":[24,58,77,94,143],"over":[25],"100":[26],"(SE)":[29],"optimization":[30],"tasks:":[31],"\"zooming\"":[32],"into":[33],"promising":[34],"regions":[35],"is":[36,125],"far":[37],"more":[38,180],"effective":[39],"than":[40,61],"under":[45],"constrained":[46],"evaluation":[47,86],"budgets.":[48,118],"Our":[49],"minimal":[50],"greedy":[51,173],"zoom":[52],"method,":[53],"EZR,":[54],"runs":[55],"three":[56],"orders":[57],"magnitude":[59],"faster":[60],"Bayesian":[65],"methods,":[66],"achieving":[67],"higher":[68],"statistical":[69],"ranks":[70],"winning":[72],"or":[73,90,105],"tying":[74],"in":[75,92,139],"84-89\\%":[76],"datasets":[78,129],"equal":[80,117],"budget.":[81],"Even":[82],"at":[83,116],"one-fifth":[84],"budget,":[87],"EZR":[88,103],"wins":[89],"ties":[91],"79-81\\%":[93],"datasets.":[95],"Surprisingly,":[96],"despite":[97],"never":[98],"explicitly":[99],"seeking":[100],"frontier,":[102],"matches":[104],"outperforms":[106],"methods":[108],"their":[110,150],"own":[111],"coverage":[112],"metrics":[113],"(IGD,":[114],"HV)":[115],"The":[119],"explanation":[120],"for":[121],"this":[122,153],"widespread":[123],"failure":[124],"structural:":[126],"across":[127],"studied,":[130],"Pareto-optimal":[131],"solutions":[132],"form":[133],"tiny,":[135],"tight":[136],"island":[137],"concentrated":[138],"compact":[141],"region":[142],"decision":[144],"space.":[145],"Methods":[146],"that":[147],"wander":[148],"waste":[149],"budgets":[151],"outside":[152],"island.":[154],"Beyond":[155],"efficiency,":[156],"zooming":[157],"yields":[158],"small,":[159],"interpretable":[160],"models,":[161],"thus":[162],"addressing":[163],"concerns":[164],"about":[165],"black-box":[166],"AI.":[167],"By":[168],"replacing":[169],"wandering":[171],"with":[172],"zooming,":[174],"we":[175],"make":[176],"SBSE":[177,189],"much":[178],"faster,":[179],"explicable,":[181],"hence":[183],"accessible":[184],"to":[185],"wider":[187],"audience.":[188],"practitioners":[190],"researchers":[192],"should":[193],"zoom,":[194],"not":[195],"wander.":[196]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-13T00:00:00"}