{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VSSQ5PTGFBJGKMDDN7UYWN7M3T","short_pith_number":"pith:VSSQ5PTG","schema_version":"1.0","canonical_sha256":"aca50ebe6628526530636fe98b37ecdcd98275b1e5f40ed55e2f264d8bcdb87f","source":{"kind":"arxiv","id":"1805.11014","version":1},"attestation_state":"computed","paper":{"title":"Evolutionary Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"David W. Corne, Michael A. Lones","submitted_at":"2018-05-28T16:13:08Z","abstract_excerpt":"Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially 'evolving' that population over time. Particularly well-known categories of EAs include genetic algorithms (GAs), Genetic Programming (GP), and Evolution Strategies (ES). EAs have proven very successful in practical a"},"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":"1805.11014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-05-28T16:13:08Z","cross_cats_sorted":[],"title_canon_sha256":"c9eb6fead1909b0269786304cc2b884e558624c7a2052e33323dfd2542f596e7","abstract_canon_sha256":"e0fa4b59a9281a397ac62eda57a0d021f1d488cbeebdabf8348f30fee87de392"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:48.504371Z","signature_b64":"rymMXpwPD4suKte2GHrRYxVRXu0jCgGZcZNppYnY0I40PNK5/vJGhItIBVcxP6TecCQ97xewoS1GfISKfWKeCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aca50ebe6628526530636fe98b37ecdcd98275b1e5f40ed55e2f264d8bcdb87f","last_reissued_at":"2026-05-18T00:14:48.503669Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:48.503669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evolutionary Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"David W. Corne, Michael A. Lones","submitted_at":"2018-05-28T16:13:08Z","abstract_excerpt":"Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially 'evolving' that population over time. Particularly well-known categories of EAs include genetic algorithms (GAs), Genetic Programming (GP), and Evolution Strategies (ES). EAs have proven very successful in practical a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.11014","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":"1805.11014","created_at":"2026-05-18T00:14:48.503774+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.11014v1","created_at":"2026-05-18T00:14:48.503774+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.11014","created_at":"2026-05-18T00:14:48.503774+00:00"},{"alias_kind":"pith_short_12","alias_value":"VSSQ5PTGFBJG","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VSSQ5PTGFBJGKMDD","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VSSQ5PTG","created_at":"2026-05-18T12:32:59.047623+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/VSSQ5PTGFBJGKMDDN7UYWN7M3T","json":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T.json","graph_json":"https://pith.science/api/pith-number/VSSQ5PTGFBJGKMDDN7UYWN7M3T/graph.json","events_json":"https://pith.science/api/pith-number/VSSQ5PTGFBJGKMDDN7UYWN7M3T/events.json","paper":"https://pith.science/paper/VSSQ5PTG"},"agent_actions":{"view_html":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T","download_json":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T.json","view_paper":"https://pith.science/paper/VSSQ5PTG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.11014&json=true","fetch_graph":"https://pith.science/api/pith-number/VSSQ5PTGFBJGKMDDN7UYWN7M3T/graph.json","fetch_events":"https://pith.science/api/pith-number/VSSQ5PTGFBJGKMDDN7UYWN7M3T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T/action/storage_attestation","attest_author":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T/action/author_attestation","sign_citation":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T/action/citation_signature","submit_replication":"https://pith.science/pith/VSSQ5PTGFBJGKMDDN7UYWN7M3T/action/replication_record"}},"created_at":"2026-05-18T00:14:48.503774+00:00","updated_at":"2026-05-18T00:14:48.503774+00:00"}