{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PUTWLNHQYPX6C7JPASZSE6MSCH","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":"b7d8d0a4e4d3e9dae6c2358d86c5117eaf7c269e7f631fcae2bc53b7e7f4fec1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2022-11-05T11:19:50Z","title_canon_sha256":"2a0a167468e693daac7f820843664ea9c462fa56e3a06ca36bde821a07b11c93"},"schema_version":"1.0","source":{"id":"2211.02879","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.02879","created_at":"2026-07-05T05:13:41Z"},{"alias_kind":"arxiv_version","alias_value":"2211.02879v1","created_at":"2026-07-05T05:13:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.02879","created_at":"2026-07-05T05:13:41Z"},{"alias_kind":"pith_short_12","alias_value":"PUTWLNHQYPX6","created_at":"2026-07-05T05:13:41Z"},{"alias_kind":"pith_short_16","alias_value":"PUTWLNHQYPX6C7JP","created_at":"2026-07-05T05:13:41Z"},{"alias_kind":"pith_short_8","alias_value":"PUTWLNHQ","created_at":"2026-07-05T05:13:41Z"}],"graph_snapshots":[{"event_id":"sha256:c5040738c6aa7d8aede5810b0c60da7d151461d00489406bbbf7f23a698df712","target":"graph","created_at":"2026-07-05T05:13:41Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2211.02879/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems. Specifically, it applies a hierarchical multi-output Gaussian process to capture ","authors_text":"Ke Li, Renzhi Chen, Xin Yao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2022-11-05T11:19:50Z","title":"A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.02879","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:f33e55b267f6ac5c03e0e486a26570d209aa37c5ecc8a5c7fb90dc7fdc1554e7","target":"record","created_at":"2026-07-05T05:13:41Z","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":"b7d8d0a4e4d3e9dae6c2358d86c5117eaf7c269e7f631fcae2bc53b7e7f4fec1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2022-11-05T11:19:50Z","title_canon_sha256":"2a0a167468e693daac7f820843664ea9c462fa56e3a06ca36bde821a07b11c93"},"schema_version":"1.0","source":{"id":"2211.02879","kind":"arxiv","version":1}},"canonical_sha256":"7d2765b4f0c3efe17d2f04b322799211c7454e787e6dd7465f6aa59f3d02cf83","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7d2765b4f0c3efe17d2f04b322799211c7454e787e6dd7465f6aa59f3d02cf83","first_computed_at":"2026-07-05T05:13:41.516070Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:13:41.516070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MG/98kO5ESBV8O7qZLuAeG8wo+Ht2GpfXo210p3n0fyjOUhfOu4KKA+4C+dTy/i6SEuwTPhexo1p2tJiafgxBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:13:41.516511Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.02879","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f33e55b267f6ac5c03e0e486a26570d209aa37c5ecc8a5c7fb90dc7fdc1554e7","sha256:c5040738c6aa7d8aede5810b0c60da7d151461d00489406bbbf7f23a698df712"],"state_sha256":"ba4935e85e5dd100053fb8cc0d6cff950c0fa8de3195bc70ab0996138fc8b92d"}