{"id":"https://openalex.org/W7167907274","doi":"https://doi.org/10.1145/3795095.3805076","title":"Multi-Objective Evolution of Patch Tokens for Tabular Transformers","display_name":"Multi-Objective Evolution of Patch Tokens for Tabular Transformers","publication_year":2026,"publication_date":"2026-07-10","ids":{"openalex":"https://openalex.org/W7167907274","doi":"https://doi.org/10.1145/3795095.3805076"},"language":null,"primary_location":{"id":"doi:10.1145/3795095.3805076","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3795095.3805076","pdf_url":null,"source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3795095.3805076","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018311744","display_name":"Han Joon Byun","orcid":"https://orcid.org/0000-0003-4891-4365"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Han Joon Byun","raw_affiliation_strings":["Seoul National University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-4891-4365","affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010372260","display_name":"Joon Huh","orcid":"https://orcid.org/0000-0001-6297-6402"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Joon Huh","raw_affiliation_strings":["Seoul National University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-6297-6402","affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120345653","display_name":"Jieun Yook","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jieun Yook","raw_affiliation_strings":["Seoul National University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0007-9245-1368","affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022911578","display_name":"Byung-Ro Moon","orcid":"https://orcid.org/0000-0002-4968-2159"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byung-ro Moon","raw_affiliation_strings":["Seoul National University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-4968-2159","affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139264467"],"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":"252","last_page":"260"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.26249998807907104,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.26249998807907104,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.18619999289512634,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.16920000314712524,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/security-token","display_name":"Security token","score":0.7713000178337097},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6779999732971191},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6524999737739563},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5745999813079834},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.5368000268936157},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.5284000039100647},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.4401000142097473},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4246000051498413},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.40709999203681946}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7713000178337097},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7031999826431274},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6779999732971191},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6524999737739563},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5745999813079834},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5368000268936157},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.5284000039100647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44859999418258667},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41600000858306885},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.40709999203681946},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3580999970436096},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.31850001215934753},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3057999908924103},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3025999963283539},{"id":"https://openalex.org/C21782646","wikidata":"https://www.wikidata.org/wiki/Q841666","display_name":"Search cost","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C194147245","wikidata":"https://www.wikidata.org/wiki/Q1076368","display_name":"Chord (peer-to-peer)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C3017489831","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Running time","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3795095.3805076","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3795095.3805076","pdf_url":null,"source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3795095.3805076","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3795095.3805076","pdf_url":null,"source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1482125571","https://openalex.org/W1525375343","https://openalex.org/W1553373771","https://openalex.org/W1990836268","https://openalex.org/W2109865546","https://openalex.org/W2126105956","https://openalex.org/W2143381319","https://openalex.org/W2144982506","https://openalex.org/W2154053567","https://openalex.org/W2295739661","https://openalex.org/W2343420905","https://openalex.org/W2750075801","https://openalex.org/W2898085636","https://openalex.org/W2998216295","https://openalex.org/W3033529678","https://openalex.org/W3094502228","https://openalex.org/W3111356309","https://openalex.org/W3138516171","https://openalex.org/W3142848995","https://openalex.org/W3168124404","https://openalex.org/W3170720134","https://openalex.org/W3174086521","https://openalex.org/W3216660278","https://openalex.org/W4234698323","https://openalex.org/W4286231567","https://openalex.org/W4288108355","https://openalex.org/W4295838474","https://openalex.org/W4302009014","https://openalex.org/W4306886919","https://openalex.org/W4309793872","https://openalex.org/W4376167308","https://openalex.org/W4383220521","https://openalex.org/W4387011164","https://openalex.org/W4402639068","https://openalex.org/W4406170795","https://openalex.org/W4406222863","https://openalex.org/W4408012823","https://openalex.org/W4415796772"],"related_works":[],"abstract_inverted_index":{"Tabular":[0],"Transformers":[1],"typically":[2],"tokenize":[3],"each":[4,43],"feature":[5,53,67],"as":[6,22,45,137],"a":[7,32,46,51,66,73,81,90,138],"token,":[8],"so":[9],"sequence":[10],"length":[11],"grows":[12],"with":[13,75,89,156],"the":[14,127,157,178],"number":[15],"of":[16,130],"columns":[17,37],"and":[18,41,60,70,110,116,125,146,167,173],"attention":[19,119,131],"cost":[20,120],"scales":[21],"O(F2).":[23],"We":[24],"introduce":[25],"EvoPatch,":[26],"an":[27],"offline":[28],"tokenizer":[29],"that":[30,94],"learns":[31],"patch":[33,44],"tokenization:":[34],"it":[35],"groups":[36],"into":[38],"K":[39],"patches":[40,98],"represents":[42],"pooled":[47],"token.":[48],"EvoPatch":[49,140],"builds":[50],"leakage-safe":[52],"space":[54],"from":[55],"Gauss-rank":[56],"normalized":[57],"numeric":[58],"features":[59],"leave-one-out":[61],"target-encoded":[62],"categorical":[63],"features,":[64],"constructs":[65],"similarity":[68],"graph,":[69],"searches":[71],"for":[72,99,113,143],"partition":[74,104],"multi-objective":[76],"evolution.":[77],"The":[78,102],"search":[79],"maximizes":[80],"fast":[82],"validation":[83],"proxy":[84],"while":[85],"maximizing":[86],"token":[87,154],"reduction,":[88],"memetic":[91],"refinement":[92],"step":[93],"balances":[95],"information":[96],"across":[97],"stable":[100],"solutions.":[101],"selected":[103],"is":[105],"computed":[106],"once":[107],"per":[108],"dataset":[109],"reused":[111],"unchanged":[112],"both":[114],"training":[115,172],"inference,":[117],"reducing":[118],"without":[121],"additional":[122],"test-time":[123],"computation":[124],"improving":[126],"computational":[128],"efficiency":[129],"models":[132],"on":[133,148,177],"wide":[134],"tables.":[135],"Used":[136],"tokenizer,":[139],"compresses":[141],"inputs":[142],"FT-Transformer,":[144],"TabTransformer,":[145],"AutoInt":[147],"seven":[149],"public":[150],"datasets,":[151],"achieving":[152],"17.5\u201375.4%":[153],"reduction":[155],"largest":[158],"absolute":[159],"relative":[160],"change":[161],"in":[162],"accuracy/RMSE":[163],"bounded":[164],"by":[165],"3.1%,":[166],"yields":[168],"up":[169],"to":[170],"2.8\u00d7":[171],"3.0\u00d7":[174],"inference":[175],"speedup":[176],"widest":[179],"dataset.":[180]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-07-11T00:00:00"}