{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZCUMAV4C5RH5FC3SE7K74LT7DZ","short_pith_number":"pith:ZCUMAV4C","schema_version":"1.0","canonical_sha256":"c8a8c05782ec4fd28b7227d5fe2e7f1e747eddb84971a0c6ef0ed0867e432f27","source":{"kind":"arxiv","id":"2405.16494","version":1},"attestation_state":"computed","paper":{"title":"A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Aimin Zhou, Bingdong Li, Hao Hao, Xiaoqun Zhang","submitted_at":"2024-05-26T09:12:44Z","abstract_excerpt":"Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive expensive problems. Utilizing surrogate models to substitute the optimization function can significantly reduce reliance on the function evaluations during the search process, thereby lowering the optimization costs. The construction of surrogate models is a critical component in SAEAs, with numerous machine learning algorithms playing a pivotal role in the model-building phase. This paper introduces Kolmogorov-Arnold Networks (KANs) as surrogate models within SAEAs, examining their application and ef"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.16494","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-05-26T09:12:44Z","cross_cats_sorted":[],"title_canon_sha256":"0aad68bd1b478a257e8167702bc2721636b89090edda2bebc8d97347a5e81edb","abstract_canon_sha256":"c8543159c57e39c0a311b6ca52c447c5e54e12e465ecdbea516332b258b0e96f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:31.358821Z","signature_b64":"eaFcveWOXrj/mnXtnxi7dR63E8u0RMuk7Ma1j8O1puyCHqMjWdQealbgbR+hSvy0AqoTCRStsDMdMjNaV3SsAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8a8c05782ec4fd28b7227d5fe2e7f1e747eddb84971a0c6ef0ed0867e432f27","last_reissued_at":"2026-07-05T08:23:31.358362Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:31.358362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Aimin Zhou, Bingdong Li, Hao Hao, Xiaoqun Zhang","submitted_at":"2024-05-26T09:12:44Z","abstract_excerpt":"Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive expensive problems. Utilizing surrogate models to substitute the optimization function can significantly reduce reliance on the function evaluations during the search process, thereby lowering the optimization costs. The construction of surrogate models is a critical component in SAEAs, with numerous machine learning algorithms playing a pivotal role in the model-building phase. This paper introduces Kolmogorov-Arnold Networks (KANs) as surrogate models within SAEAs, examining their application and ef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.16494","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.16494/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.16494","created_at":"2026-07-05T08:23:31.358421+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.16494v1","created_at":"2026-07-05T08:23:31.358421+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.16494","created_at":"2026-07-05T08:23:31.358421+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZCUMAV4C5RH5","created_at":"2026-07-05T08:23:31.358421+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZCUMAV4C5RH5FC3S","created_at":"2026-07-05T08:23:31.358421+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZCUMAV4C","created_at":"2026-07-05T08:23:31.358421+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ","json":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ.json","graph_json":"https://pith.science/api/pith-number/ZCUMAV4C5RH5FC3SE7K74LT7DZ/graph.json","events_json":"https://pith.science/api/pith-number/ZCUMAV4C5RH5FC3SE7K74LT7DZ/events.json","paper":"https://pith.science/paper/ZCUMAV4C"},"agent_actions":{"view_html":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ","download_json":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ.json","view_paper":"https://pith.science/paper/ZCUMAV4C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.16494&json=true","fetch_graph":"https://pith.science/api/pith-number/ZCUMAV4C5RH5FC3SE7K74LT7DZ/graph.json","fetch_events":"https://pith.science/api/pith-number/ZCUMAV4C5RH5FC3SE7K74LT7DZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ/action/storage_attestation","attest_author":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ/action/author_attestation","sign_citation":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ/action/citation_signature","submit_replication":"https://pith.science/pith/ZCUMAV4C5RH5FC3SE7K74LT7DZ/action/replication_record"}},"created_at":"2026-07-05T08:23:31.358421+00:00","updated_at":"2026-07-05T08:23:31.358421+00:00"}