{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:BR2P4T6MMKR4ZK32BAVAQ5GMJY","short_pith_number":"pith:BR2P4T6M","canonical_record":{"source":{"id":"1802.00721","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-02-02T15:11:33Z","cross_cats_sorted":[],"title_canon_sha256":"aeec5d6d98e5ca84c28ed45fe96310d0ce670d9fca91c54bcecbc32627716ec7","abstract_canon_sha256":"31252ea70b47b60dce6fd1437d02fc3101135231d5687ea8f8d6012b8b0eb684"},"schema_version":"1.0"},"canonical_sha256":"0c74fe4fcc62a3ccab7a082a0874cc4e06923b797ec5cd92abacfb8f98fbb5c9","source":{"kind":"arxiv","id":"1802.00721","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.00721","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"arxiv_version","alias_value":"1802.00721v1","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00721","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"pith_short_12","alias_value":"BR2P4T6MMKR4","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BR2P4T6MMKR4ZK32","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BR2P4T6M","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:BR2P4T6MMKR4ZK32BAVAQ5GMJY","target":"record","payload":{"canonical_record":{"source":{"id":"1802.00721","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-02-02T15:11:33Z","cross_cats_sorted":[],"title_canon_sha256":"aeec5d6d98e5ca84c28ed45fe96310d0ce670d9fca91c54bcecbc32627716ec7","abstract_canon_sha256":"31252ea70b47b60dce6fd1437d02fc3101135231d5687ea8f8d6012b8b0eb684"},"schema_version":"1.0"},"canonical_sha256":"0c74fe4fcc62a3ccab7a082a0874cc4e06923b797ec5cd92abacfb8f98fbb5c9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:32.487509Z","signature_b64":"uk5qr3C4f199shOnbI1n1JlpCfeMQzErkjS2gjrnT5mP/okIQ5DkHZs84lfv+WcTmEuK+v5A2F6t5PtzkWU8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c74fe4fcc62a3ccab7a082a0874cc4e06923b797ec5cd92abacfb8f98fbb5c9","last_reissued_at":"2026-05-18T00:24:32.486911Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:32.486911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.00721","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"00zVT0N6oSlQvWlF+1+HYoLIBTNIewNycB6OYKp5cdgQzJNQRT9WLM4zgckiVNn50w/gl6dc1viKg93kTjz/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T12:03:38.822096Z"},"content_sha256":"410308b406de613f5779267ce7d75e41937a35fe47207ac921bddba643c08159","schema_version":"1.0","event_id":"sha256:410308b406de613f5779267ce7d75e41937a35fe47207ac921bddba643c08159"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:BR2P4T6MMKR4ZK32BAVAQ5GMJY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Per Kristian Lehre, Phan Trung Hai Nguyen","submitted_at":"2018-02-02T15:11:33Z","abstract_excerpt":"Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models which are learned from selected individuals. It is hoped that EDAs may improve optimisation performance on epistatic fitness landscapes by learning variable interactions. However, hardly any rigorous results are available to support claims about the performance of EDAs, even for fitness functions without epistasis. The expected runtime of the Univariate Marginal Distribution Algorithm (UMDA) on OneMax was recently show"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00721","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z4Q3pFU58mafQ2yvxgMHIB0bQS/EEt+Vx3PWOkHKu71w64psTa8g6+3c8R9E8AUfCA8Bk1sGDtuhsUriWYdRBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T12:03:38.822451Z"},"content_sha256":"e151340c8ec6a7771af9f5c0d41b7945bc325490c84137013df50765cb9bfb83","schema_version":"1.0","event_id":"sha256:e151340c8ec6a7771af9f5c0d41b7945bc325490c84137013df50765cb9bfb83"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BR2P4T6MMKR4ZK32BAVAQ5GMJY/bundle.json","state_url":"https://pith.science/pith/BR2P4T6MMKR4ZK32BAVAQ5GMJY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BR2P4T6MMKR4ZK32BAVAQ5GMJY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T12:03:38Z","links":{"resolver":"https://pith.science/pith/BR2P4T6MMKR4ZK32BAVAQ5GMJY","bundle":"https://pith.science/pith/BR2P4T6MMKR4ZK32BAVAQ5GMJY/bundle.json","state":"https://pith.science/pith/BR2P4T6MMKR4ZK32BAVAQ5GMJY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BR2P4T6MMKR4ZK32BAVAQ5GMJY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:BR2P4T6MMKR4ZK32BAVAQ5GMJY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"31252ea70b47b60dce6fd1437d02fc3101135231d5687ea8f8d6012b8b0eb684","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-02-02T15:11:33Z","title_canon_sha256":"aeec5d6d98e5ca84c28ed45fe96310d0ce670d9fca91c54bcecbc32627716ec7"},"schema_version":"1.0","source":{"id":"1802.00721","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.00721","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"arxiv_version","alias_value":"1802.00721v1","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00721","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"pith_short_12","alias_value":"BR2P4T6MMKR4","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BR2P4T6MMKR4ZK32","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BR2P4T6M","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:e151340c8ec6a7771af9f5c0d41b7945bc325490c84137013df50765cb9bfb83","target":"graph","created_at":"2026-05-18T00:24:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models which are learned from selected individuals. It is hoped that EDAs may improve optimisation performance on epistatic fitness landscapes by learning variable interactions. However, hardly any rigorous results are available to support claims about the performance of EDAs, even for fitness functions without epistasis. The expected runtime of the Univariate Marginal Distribution Algorithm (UMDA) on OneMax was recently show","authors_text":"Per Kristian Lehre, Phan Trung Hai Nguyen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-02-02T15:11:33Z","title":"Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00721","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:410308b406de613f5779267ce7d75e41937a35fe47207ac921bddba643c08159","target":"record","created_at":"2026-05-18T00:24:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"31252ea70b47b60dce6fd1437d02fc3101135231d5687ea8f8d6012b8b0eb684","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-02-02T15:11:33Z","title_canon_sha256":"aeec5d6d98e5ca84c28ed45fe96310d0ce670d9fca91c54bcecbc32627716ec7"},"schema_version":"1.0","source":{"id":"1802.00721","kind":"arxiv","version":1}},"canonical_sha256":"0c74fe4fcc62a3ccab7a082a0874cc4e06923b797ec5cd92abacfb8f98fbb5c9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c74fe4fcc62a3ccab7a082a0874cc4e06923b797ec5cd92abacfb8f98fbb5c9","first_computed_at":"2026-05-18T00:24:32.486911Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:32.486911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uk5qr3C4f199shOnbI1n1JlpCfeMQzErkjS2gjrnT5mP/okIQ5DkHZs84lfv+WcTmEuK+v5A2F6t5PtzkWU8CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:32.487509Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.00721","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:410308b406de613f5779267ce7d75e41937a35fe47207ac921bddba643c08159","sha256:e151340c8ec6a7771af9f5c0d41b7945bc325490c84137013df50765cb9bfb83"],"state_sha256":"145537890c469131956c133796391320613a9369b6bfc1b19dc8d352c4a98793"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y24rPyNzZG9mJ0wyxXPEoDtg5398hKVTiIsBPUbAtPLYmwIV7SmseMTdQ6Q3ofYxxG1U64lONDxGl/v+031dCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T12:03:38.824641Z","bundle_sha256":"b5448d3011ce9803d420fd44dc464b8b1c5cfbff2c32d90c7005ac4c6bcd66dd"}}