{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BHOJLVOEZGJTB2D37DJ6EYQ45N","short_pith_number":"pith:BHOJLVOE","schema_version":"1.0","canonical_sha256":"09dc95d5c4c99330e87bf8d3e2621ceb652b52daf01ce583e730a5aee03a7ec5","source":{"kind":"arxiv","id":"2606.19369","version":1},"attestation_state":"computed","paper":{"title":"Zero-Inflated Gaussian Distributions Enable Parameter-Space Sparsity in Estimation-of-Distribution Algorithms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andreas Faust, Juergen Becker, Sven Nitzsche","submitted_at":"2026-06-11T18:43:18Z","abstract_excerpt":"Estimation-of-distribution algorithms (EDAs) are a powerful class of evolutionary methods for black-box optimization, especially when little is known about the structure of the objective. Whereas classical evolutionary algorithms rely on hand-designed mutation and crossover operators, hard to devise for unknown problem structures, and a source of bias, EDAs sidestep operator design entirely: they fit a probability distribution to the best individuals and sample the next generation from it. EDAs are well established on continuous parameter spaces, but they have not previously been generalized t"},"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":"2606.19369","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-11T18:43:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8266dde3d91490d2d7a693aef9b5fc8fcf0c02eeacb5fbd59e73095075d95a14","abstract_canon_sha256":"8f06482ad7940b1aefe24473ff1c00e7723dc2204d0b8d56cfbb5d0c38275d3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:23.343821Z","signature_b64":"Kql9/CYsNmDERYigxcqh82CTJqS78+l79L6S5AXodJFGXkabyoNy3o1rParhm3heVUr8hrc9as0EA+gvLFEeDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09dc95d5c4c99330e87bf8d3e2621ceb652b52daf01ce583e730a5aee03a7ec5","last_reissued_at":"2026-06-19T16:12:23.343442Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:23.343442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Zero-Inflated Gaussian Distributions Enable Parameter-Space Sparsity in Estimation-of-Distribution Algorithms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andreas Faust, Juergen Becker, Sven Nitzsche","submitted_at":"2026-06-11T18:43:18Z","abstract_excerpt":"Estimation-of-distribution algorithms (EDAs) are a powerful class of evolutionary methods for black-box optimization, especially when little is known about the structure of the objective. Whereas classical evolutionary algorithms rely on hand-designed mutation and crossover operators, hard to devise for unknown problem structures, and a source of bias, EDAs sidestep operator design entirely: they fit a probability distribution to the best individuals and sample the next generation from it. EDAs are well established on continuous parameter spaces, but they have not previously been generalized t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19369","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/2606.19369/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":"2606.19369","created_at":"2026-06-19T16:12:23.343497+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.19369v1","created_at":"2026-06-19T16:12:23.343497+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19369","created_at":"2026-06-19T16:12:23.343497+00:00"},{"alias_kind":"pith_short_12","alias_value":"BHOJLVOEZGJT","created_at":"2026-06-19T16:12:23.343497+00:00"},{"alias_kind":"pith_short_16","alias_value":"BHOJLVOEZGJTB2D3","created_at":"2026-06-19T16:12:23.343497+00:00"},{"alias_kind":"pith_short_8","alias_value":"BHOJLVOE","created_at":"2026-06-19T16:12:23.343497+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/BHOJLVOEZGJTB2D37DJ6EYQ45N","json":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N.json","graph_json":"https://pith.science/api/pith-number/BHOJLVOEZGJTB2D37DJ6EYQ45N/graph.json","events_json":"https://pith.science/api/pith-number/BHOJLVOEZGJTB2D37DJ6EYQ45N/events.json","paper":"https://pith.science/paper/BHOJLVOE"},"agent_actions":{"view_html":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N","download_json":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N.json","view_paper":"https://pith.science/paper/BHOJLVOE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.19369&json=true","fetch_graph":"https://pith.science/api/pith-number/BHOJLVOEZGJTB2D37DJ6EYQ45N/graph.json","fetch_events":"https://pith.science/api/pith-number/BHOJLVOEZGJTB2D37DJ6EYQ45N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N/action/storage_attestation","attest_author":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N/action/author_attestation","sign_citation":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N/action/citation_signature","submit_replication":"https://pith.science/pith/BHOJLVOEZGJTB2D37DJ6EYQ45N/action/replication_record"}},"created_at":"2026-06-19T16:12:23.343497+00:00","updated_at":"2026-06-19T16:12:23.343497+00:00"}