{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NXAGYGFA3D3P4BQDQLBW2TJDPQ","short_pith_number":"pith:NXAGYGFA","schema_version":"1.0","canonical_sha256":"6dc06c18a0d8f6fe060382c36d4d237c3d08f3e7d09a340f04881d2abaed0d21","source":{"kind":"arxiv","id":"1905.13266","version":1},"attestation_state":"computed","paper":{"title":"Epsilon-Lexicase Selection for Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Kourosh Danai, Lee Spector, William La Cava","submitted_at":"2019-05-30T19:10:29Z","abstract_excerpt":"Lexicase selection is a parent selection method that considers test cases separately, rather than in aggregate, when performing parent selection. It performs well in discrete error spaces but not on the continuous-valued problems that compose most system identification tasks. In this paper, we develop a new form of lexicase selection for symbolic regression, named epsilon-lexicase selection, that redefines the pass condition for individuals on each test case in a more effective way. We run a series of experiments on real-world and synthetic problems with several treatments of epsilon and quant"},"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":"1905.13266","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-30T19:10:29Z","cross_cats_sorted":[],"title_canon_sha256":"b5fdba657f3b47c7c85f6c021cb565f71dbd6d28517fee55450d01087129a19e","abstract_canon_sha256":"bcc53347cf04147c00464185c3bfd1fb43ec9d9158d7f9c352c8a480ab695bd5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:35.473928Z","signature_b64":"N6B/AlXmUnWrCEPYWr3HmK5KaL14QvGpDlq+U/4joleao7oQoIjw2xS+aBt+pNU8DdrhpdKwVRFYCjse1C3iDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6dc06c18a0d8f6fe060382c36d4d237c3d08f3e7d09a340f04881d2abaed0d21","last_reissued_at":"2026-05-17T23:44:35.473494Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:35.473494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Epsilon-Lexicase Selection for Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Kourosh Danai, Lee Spector, William La Cava","submitted_at":"2019-05-30T19:10:29Z","abstract_excerpt":"Lexicase selection is a parent selection method that considers test cases separately, rather than in aggregate, when performing parent selection. It performs well in discrete error spaces but not on the continuous-valued problems that compose most system identification tasks. In this paper, we develop a new form of lexicase selection for symbolic regression, named epsilon-lexicase selection, that redefines the pass condition for individuals on each test case in a more effective way. We run a series of experiments on real-world and synthetic problems with several treatments of epsilon and quant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.13266","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":""},"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":"1905.13266","created_at":"2026-05-17T23:44:35.473560+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.13266v1","created_at":"2026-05-17T23:44:35.473560+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.13266","created_at":"2026-05-17T23:44:35.473560+00:00"},{"alias_kind":"pith_short_12","alias_value":"NXAGYGFA3D3P","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"NXAGYGFA3D3P4BQD","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"NXAGYGFA","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2512.07961","citing_title":"Towards symbolic regression for interpretable clinical decision scores","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ","json":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ.json","graph_json":"https://pith.science/api/pith-number/NXAGYGFA3D3P4BQDQLBW2TJDPQ/graph.json","events_json":"https://pith.science/api/pith-number/NXAGYGFA3D3P4BQDQLBW2TJDPQ/events.json","paper":"https://pith.science/paper/NXAGYGFA"},"agent_actions":{"view_html":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ","download_json":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ.json","view_paper":"https://pith.science/paper/NXAGYGFA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.13266&json=true","fetch_graph":"https://pith.science/api/pith-number/NXAGYGFA3D3P4BQDQLBW2TJDPQ/graph.json","fetch_events":"https://pith.science/api/pith-number/NXAGYGFA3D3P4BQDQLBW2TJDPQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ/action/storage_attestation","attest_author":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ/action/author_attestation","sign_citation":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ/action/citation_signature","submit_replication":"https://pith.science/pith/NXAGYGFA3D3P4BQDQLBW2TJDPQ/action/replication_record"}},"created_at":"2026-05-17T23:44:35.473560+00:00","updated_at":"2026-05-17T23:44:35.473560+00:00"}