{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5IWEWS62SPUXIEW3KUS6WQZE2C","short_pith_number":"pith:5IWEWS62","schema_version":"1.0","canonical_sha256":"ea2c4b4bda93e97412db5525eb4324d0b30bceacae7904071df707c05ae1fca0","source":{"kind":"arxiv","id":"2605.30954","version":1},"attestation_state":"computed","paper":{"title":"GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Anton Bouter, Jasper Post, Johannes Koch, Peter A.N. Bosman, Tanja Alderliesten","submitted_at":"2026-05-29T07:48:46Z","abstract_excerpt":"GP-GOMEA is a state-of-the-art evolutionary algorithm for symbolic regression, known for discovering small and interpretable models. However, its computational cost remains substantial, limiting its applicability to larger datasets and more complex target expressions. In contrast, the rise of modern subsymbolic approaches, particularly deep learning, has been driven largely by the massive parallelism offered by GPUs. In this work, we take the first major step toward a fully GPU-accelerated GP-GOMEA by introducing a GPU-based fitness evaluation scheme. We design a GPU-friendly representation of"},"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":"2605.30954","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.NE","submitted_at":"2026-05-29T07:48:46Z","cross_cats_sorted":[],"title_canon_sha256":"8c7578a6dc9470dbb95dc0c1060b3dd649cc44d9dd81a46162fd1e741bd8ea2e","abstract_canon_sha256":"87f2d9b2874a7f53e30e6acf6aa282eee8be7a7d7644f8ac8966b04bd5a4477c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:27.369454Z","signature_b64":"UgQ8wEZ8uj4EH4WXDJINuKzSom3TXOmOrw4BIDhzdmURKeUmkut549mv2nQq3nXTAdRhG7pyH6UKd0hdB4qMBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea2c4b4bda93e97412db5525eb4324d0b30bceacae7904071df707c05ae1fca0","last_reissued_at":"2026-06-01T01:03:27.368956Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:27.368956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Anton Bouter, Jasper Post, Johannes Koch, Peter A.N. Bosman, Tanja Alderliesten","submitted_at":"2026-05-29T07:48:46Z","abstract_excerpt":"GP-GOMEA is a state-of-the-art evolutionary algorithm for symbolic regression, known for discovering small and interpretable models. However, its computational cost remains substantial, limiting its applicability to larger datasets and more complex target expressions. In contrast, the rise of modern subsymbolic approaches, particularly deep learning, has been driven largely by the massive parallelism offered by GPUs. In this work, we take the first major step toward a fully GPU-accelerated GP-GOMEA by introducing a GPU-based fitness evaluation scheme. We design a GPU-friendly representation of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30954","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/2605.30954/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":"2605.30954","created_at":"2026-06-01T01:03:27.369028+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.30954v1","created_at":"2026-06-01T01:03:27.369028+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30954","created_at":"2026-06-01T01:03:27.369028+00:00"},{"alias_kind":"pith_short_12","alias_value":"5IWEWS62SPUX","created_at":"2026-06-01T01:03:27.369028+00:00"},{"alias_kind":"pith_short_16","alias_value":"5IWEWS62SPUXIEW3","created_at":"2026-06-01T01:03:27.369028+00:00"},{"alias_kind":"pith_short_8","alias_value":"5IWEWS62","created_at":"2026-06-01T01:03:27.369028+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/5IWEWS62SPUXIEW3KUS6WQZE2C","json":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C.json","graph_json":"https://pith.science/api/pith-number/5IWEWS62SPUXIEW3KUS6WQZE2C/graph.json","events_json":"https://pith.science/api/pith-number/5IWEWS62SPUXIEW3KUS6WQZE2C/events.json","paper":"https://pith.science/paper/5IWEWS62"},"agent_actions":{"view_html":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C","download_json":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C.json","view_paper":"https://pith.science/paper/5IWEWS62","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.30954&json=true","fetch_graph":"https://pith.science/api/pith-number/5IWEWS62SPUXIEW3KUS6WQZE2C/graph.json","fetch_events":"https://pith.science/api/pith-number/5IWEWS62SPUXIEW3KUS6WQZE2C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C/action/storage_attestation","attest_author":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C/action/author_attestation","sign_citation":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C/action/citation_signature","submit_replication":"https://pith.science/pith/5IWEWS62SPUXIEW3KUS6WQZE2C/action/replication_record"}},"created_at":"2026-06-01T01:03:27.369028+00:00","updated_at":"2026-06-01T01:03:27.369028+00:00"}