{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TC5FKH3DDYRTX2JLKUH3AYLRY3","short_pith_number":"pith:TC5FKH3D","schema_version":"1.0","canonical_sha256":"98ba551f631e233be92b550fb06171c6cd7039ec37144a0f5bfc981f4523f95c","source":{"kind":"arxiv","id":"2301.01488","version":2},"attestation_state":"computed","paper":{"title":"Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Alexander Lalejini, Charles Ofria, Dominik Sobania, Franz Rothlauf, Lee Spector, Martin Briesch, Ryan Boldi, Thomas Helmuth","submitted_at":"2023-01-04T08:47:18Z","abstract_excerpt":"Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, creating a down-sample randomly might exclude important cases from the current down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused despite their redundancy. In this work, we introduce Inf"},"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":"2301.01488","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2023-01-04T08:47:18Z","cross_cats_sorted":[],"title_canon_sha256":"1d13b63e5d3722bcb25ff299626c4ba1dd5628420f2728e3bb413fd485dae4e3","abstract_canon_sha256":"ac2c7b4d8574c0c8c45fb02f2a4e558303e49a7acacb969571a51f1bf8345052"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:54.988176Z","signature_b64":"z+EW2mF4EXQHfxp8AUkj87r2PBnXeZA8XVSnkrQSF1JkzcuDPOlLCiltaHK9TK73pufYyZnkfqPD/3i4ccAbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98ba551f631e233be92b550fb06171c6cd7039ec37144a0f5bfc981f4523f95c","last_reissued_at":"2026-07-05T07:47:54.987760Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:54.987760Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Alexander Lalejini, Charles Ofria, Dominik Sobania, Franz Rothlauf, Lee Spector, Martin Briesch, Ryan Boldi, Thomas Helmuth","submitted_at":"2023-01-04T08:47:18Z","abstract_excerpt":"Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, creating a down-sample randomly might exclude important cases from the current down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused despite their redundancy. In this work, we introduce Inf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.01488","kind":"arxiv","version":2},"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/2301.01488/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":"2301.01488","created_at":"2026-07-05T07:47:54.987813+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.01488v2","created_at":"2026-07-05T07:47:54.987813+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.01488","created_at":"2026-07-05T07:47:54.987813+00:00"},{"alias_kind":"pith_short_12","alias_value":"TC5FKH3DDYRT","created_at":"2026-07-05T07:47:54.987813+00:00"},{"alias_kind":"pith_short_16","alias_value":"TC5FKH3DDYRTX2JL","created_at":"2026-07-05T07:47:54.987813+00:00"},{"alias_kind":"pith_short_8","alias_value":"TC5FKH3D","created_at":"2026-07-05T07:47:54.987813+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/TC5FKH3DDYRTX2JLKUH3AYLRY3","json":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3.json","graph_json":"https://pith.science/api/pith-number/TC5FKH3DDYRTX2JLKUH3AYLRY3/graph.json","events_json":"https://pith.science/api/pith-number/TC5FKH3DDYRTX2JLKUH3AYLRY3/events.json","paper":"https://pith.science/paper/TC5FKH3D"},"agent_actions":{"view_html":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3","download_json":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3.json","view_paper":"https://pith.science/paper/TC5FKH3D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.01488&json=true","fetch_graph":"https://pith.science/api/pith-number/TC5FKH3DDYRTX2JLKUH3AYLRY3/graph.json","fetch_events":"https://pith.science/api/pith-number/TC5FKH3DDYRTX2JLKUH3AYLRY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3/action/storage_attestation","attest_author":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3/action/author_attestation","sign_citation":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3/action/citation_signature","submit_replication":"https://pith.science/pith/TC5FKH3DDYRTX2JLKUH3AYLRY3/action/replication_record"}},"created_at":"2026-07-05T07:47:54.987813+00:00","updated_at":"2026-07-05T07:47:54.987813+00:00"}