{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DOKHWQENWWSFICJLUUW5VQSQG5","short_pith_number":"pith:DOKHWQEN","schema_version":"1.0","canonical_sha256":"1b947b408db5a454092ba52ddac250374022c1ce2ccf56e2126c6c2b2aef8ff6","source":{"kind":"arxiv","id":"2405.03419","version":1},"attestation_state":"computed","paper":{"title":"Automated Metaheuristic Algorithm Design with Autoregressive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Bai Yan, Jian Yang, Qiqi Duan, Qi Zhao, Tengfei Liu, Yuhui Shi","submitted_at":"2024-05-06T12:36:17Z","abstract_excerpt":"Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structure and operate from scratch. This poses a clear gap towards fully discovering potentials over the metaheuristic family and fertilizing from prior design experience. To bridge the gap, this paper proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms. Our designer formulates metaheuristic algorithm design as a sequence generation task, an"},"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":"2405.03419","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2024-05-06T12:36:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1f11cf20039021bd47287b23d483d80499cb8882e6d5ae50e220e7b4388cbba9","abstract_canon_sha256":"82885ea01c19a6af15354dfd7e516a694315d54c83dde989a4bd77167faf14a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:02.821243Z","signature_b64":"hDLQ3vFtC0mKv6OSkuHfozEel46kadK1BDrveVsEdLq42mKgCNugEZmXnDP8EOzoKQy9nUghq38xJKxF/vPqBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b947b408db5a454092ba52ddac250374022c1ce2ccf56e2126c6c2b2aef8ff6","last_reissued_at":"2026-07-05T08:16:02.820883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:02.820883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated Metaheuristic Algorithm Design with Autoregressive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Bai Yan, Jian Yang, Qiqi Duan, Qi Zhao, Tengfei Liu, Yuhui Shi","submitted_at":"2024-05-06T12:36:17Z","abstract_excerpt":"Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structure and operate from scratch. This poses a clear gap towards fully discovering potentials over the metaheuristic family and fertilizing from prior design experience. To bridge the gap, this paper proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms. Our designer formulates metaheuristic algorithm design as a sequence generation task, an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03419","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/2405.03419/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":"2405.03419","created_at":"2026-07-05T08:16:02.820937+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.03419v1","created_at":"2026-07-05T08:16:02.820937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03419","created_at":"2026-07-05T08:16:02.820937+00:00"},{"alias_kind":"pith_short_12","alias_value":"DOKHWQENWWSF","created_at":"2026-07-05T08:16:02.820937+00:00"},{"alias_kind":"pith_short_16","alias_value":"DOKHWQENWWSFICJL","created_at":"2026-07-05T08:16:02.820937+00:00"},{"alias_kind":"pith_short_8","alias_value":"DOKHWQEN","created_at":"2026-07-05T08:16:02.820937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.02010","citing_title":"Meta-Black-Box-Optimization through Offline Q-function Learning","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5","json":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5.json","graph_json":"https://pith.science/api/pith-number/DOKHWQENWWSFICJLUUW5VQSQG5/graph.json","events_json":"https://pith.science/api/pith-number/DOKHWQENWWSFICJLUUW5VQSQG5/events.json","paper":"https://pith.science/paper/DOKHWQEN"},"agent_actions":{"view_html":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5","download_json":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5.json","view_paper":"https://pith.science/paper/DOKHWQEN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.03419&json=true","fetch_graph":"https://pith.science/api/pith-number/DOKHWQENWWSFICJLUUW5VQSQG5/graph.json","fetch_events":"https://pith.science/api/pith-number/DOKHWQENWWSFICJLUUW5VQSQG5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5/action/storage_attestation","attest_author":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5/action/author_attestation","sign_citation":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5/action/citation_signature","submit_replication":"https://pith.science/pith/DOKHWQENWWSFICJLUUW5VQSQG5/action/replication_record"}},"created_at":"2026-07-05T08:16:02.820937+00:00","updated_at":"2026-07-05T08:16:02.820937+00:00"}