{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5QS7G2HT4Z5U33765SVBBV4KLH","short_pith_number":"pith:5QS7G2HT","schema_version":"1.0","canonical_sha256":"ec25f368f3e67b4deffeecaa10d78a59e1bb83da270a52f3169fb56ac5856796","source":{"kind":"arxiv","id":"1811.11597","version":1},"attestation_state":"computed","paper":{"title":"Automated Algorithm Selection: Survey and Perspectives","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Frank Neumann, Heike Trautmann, Holger H. Hoos, Pascal Kerschke","submitted_at":"2018-11-28T14:43:49Z","abstract_excerpt":"It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The "},"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":"1811.11597","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-28T14:43:49Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"2245f25d42b3a47b88377e54ee86c00a00c84d89c6a5c41467ad81ce4875d921","abstract_canon_sha256":"b12fec2a6da1d010967c3c51bba8297b74541c4b854276e7331d55928f77de75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:40.575888Z","signature_b64":"9z/5IuBLTO7zDhdz51froPwphbYvR2puC1gr6sgRD/F2IzA110O+mNIvV4uZ9v3XI7WCE2fTKbKmpvwLD+7zCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec25f368f3e67b4deffeecaa10d78a59e1bb83da270a52f3169fb56ac5856796","last_reissued_at":"2026-05-17T23:59:40.575190Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:40.575190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated Algorithm Selection: Survey and Perspectives","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Frank Neumann, Heike Trautmann, Holger H. Hoos, Pascal Kerschke","submitted_at":"2018-11-28T14:43:49Z","abstract_excerpt":"It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11597","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1811.11597","created_at":"2026-05-17T23:59:40.575293+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.11597v1","created_at":"2026-05-17T23:59:40.575293+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11597","created_at":"2026-05-17T23:59:40.575293+00:00"},{"alias_kind":"pith_short_12","alias_value":"5QS7G2HT4Z5U","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5QS7G2HT4Z5U3376","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5QS7G2HT","created_at":"2026-05-18T12:32:08.215937+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/5QS7G2HT4Z5U33765SVBBV4KLH","json":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH.json","graph_json":"https://pith.science/api/pith-number/5QS7G2HT4Z5U33765SVBBV4KLH/graph.json","events_json":"https://pith.science/api/pith-number/5QS7G2HT4Z5U33765SVBBV4KLH/events.json","paper":"https://pith.science/paper/5QS7G2HT"},"agent_actions":{"view_html":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH","download_json":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH.json","view_paper":"https://pith.science/paper/5QS7G2HT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.11597&json=true","fetch_graph":"https://pith.science/api/pith-number/5QS7G2HT4Z5U33765SVBBV4KLH/graph.json","fetch_events":"https://pith.science/api/pith-number/5QS7G2HT4Z5U33765SVBBV4KLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH/action/storage_attestation","attest_author":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH/action/author_attestation","sign_citation":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH/action/citation_signature","submit_replication":"https://pith.science/pith/5QS7G2HT4Z5U33765SVBBV4KLH/action/replication_record"}},"created_at":"2026-05-17T23:59:40.575293+00:00","updated_at":"2026-05-17T23:59:40.575293+00:00"}