{"id":"https://openalex.org/W7168282196","doi":"https://doi.org/10.48550/arxiv.2607.11358","title":"RefineEvo: Planning-Guided Heuristic Evolution with Bidirectional Experience","display_name":"RefineEvo: Planning-Guided Heuristic Evolution with Bidirectional Experience","publication_year":2026,"publication_date":"2026-07-13","ids":{"openalex":"https://openalex.org/W7168282196","doi":"https://doi.org/10.48550/arxiv.2607.11358"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.11358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.11358","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":null,"license_id":null,"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.2607.11358","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140648124","display_name":"Yang Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140679593","display_name":"Junran Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Junran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140685421","display_name":"Yifan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140630746","display_name":"Ning Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Ning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140655371","display_name":"Fanshuo Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Fanshuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140614536","display_name":"Jian Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Jian","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":0.2856999933719635,"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":0.2856999933719635,"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":0.1590999960899353,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.08160000294446945,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/leverage","display_name":"Leverage (statistics)","score":0.6789000034332275},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.5835000276565552},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.5271999835968018},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.5037999749183655},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4779999852180481},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4345000088214874},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4219000041484833},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.41920000314712524}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6789000034332275},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6783999800682068},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.5835000276565552},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.5271999835968018},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.41920000314712524},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40389999747276306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3783999979496002},{"id":"https://openalex.org/C70587473","wikidata":"https://www.wikidata.org/wiki/Q7834111","display_name":"Transformative learning","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3343000113964081},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C52692508","wikidata":"https://www.wikidata.org/wiki/Q1333872","display_name":"Combinatorial optimization","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C125583679","wikidata":"https://www.wikidata.org/wiki/Q755673","display_name":"Search algorithm","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2632000148296356},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C121835503","wikidata":"https://www.wikidata.org/wiki/Q2596288","display_name":"Evolutionary programming","level":3,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.11358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.11358","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.11358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.11358","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"Heuristic":[1],"Design":[2],"(AHD)":[3],"has":[4],"emerged":[5],"as":[6],"a":[7,46,54,59,65,82,89],"transformative":[8],"approach":[9],"for":[10],"solving":[11],"combinatorial":[12,130],"optimization":[13,131],"problems.":[14],"While":[15],"recent":[16],"Large":[17],"Language":[18],"Model":[19],"(LLM)-based":[20],"methods":[21],"have":[22],"shown":[23],"promise,":[24],"they":[25],"predominantly":[26],"rely":[27],"on":[28,76,127],"fixed":[29],"evolutionary":[30,48,70],"operators":[31,71],"and":[32,37,72,81,97,118,154],"struggle":[33],"to":[34,67,84,106,111,123],"effectively":[35],"accumulate":[36],"reuse":[38],"historical":[39],"search":[40,79,109],"experience.":[41],"This":[42,100],"paper":[43],"proposes":[44],"RefineEvo,":[45],"novel":[47],"framework":[49,102],"that":[50,134],"transforms":[51],"AHD":[52],"from":[53],"static":[55],"trial-and-error":[56],"process":[57],"into":[58,88],"planning-guided,":[60],"experience-driven":[61],"system.":[62],"RefineEvo":[63,135,142],"introduces":[64],"Planner":[66],"dynamically":[68],"schedule":[69],"trigger":[73],"refinement":[74],"based":[75],"the":[77,104,112,116],"current":[78],"state,":[80],"Reflector":[83],"distill":[85],"valuable":[86],"lessons":[87],"Bidirectional":[90],"Experience":[91],"Pool":[92],"containing":[93],"both":[94],"positive":[95],"insights":[96,122],"negative":[98],"pitfalls.":[99],"synergistic":[101],"enables":[103],"system":[105],"adapt":[107],"its":[108],"tools":[110],"evolving":[113],"complexity":[114],"of":[115],"problem":[117],"leverage":[119],"trajectory-aware,":[120],"situation-conditioned":[121],"guide":[124],"generation.":[125],"Experiments":[126],"several":[128],"classic":[129],"benchmarks":[132],"demonstrate":[133],"consistently":[136],"outperforms":[137],"strong":[138],"baselines.":[139],"In":[140],"particular,":[141],"delivers":[143],"superior":[144],"solution":[145],"quality":[146],"while":[147],"improving":[148],"token":[149],"efficiency,":[150],"enabling":[151],"more":[152],"efficient":[153],"autonomous":[155],"heuristic":[156],"design.":[157]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}
