{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:5I42ME7LF4E6SDEX2M3KTU4IIB","short_pith_number":"pith:5I42ME7L","schema_version":"1.0","canonical_sha256":"ea39a613eb2f09e90c97d336a9d388406684099f6d851f74c9e3c983f744aecb","source":{"kind":"arxiv","id":"1311.4987","version":1},"attestation_state":"computed","paper":{"title":"Analyzing Evolutionary Optimization in Noisy Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AI","authors_text":"Chao Qian, Yang Yu, Zhi-Hua Zhou","submitted_at":"2013-11-20T09:28:52Z","abstract_excerpt":"Many optimization tasks have to be handled in noisy environments, where we cannot obtain the exact evaluation of a solution but only a noisy one. For noisy optimization tasks, evolutionary algorithms (EAs), a kind of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on empirical studying and designing EAs for noisy optimization, while, the theoretical counterpart has been little investigated. In this paper, we investigate a largely ignored question, i.e., whether an optimization problem will always become harder for EAs in a nois"},"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":"1311.4987","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-11-20T09:28:52Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"ccd324f10ead954a80273e923fc8a20301188695ce3a47a482b9fe8994595841","abstract_canon_sha256":"e5cf72c3cee127b92a939c40f78e27c7e01cda9f66b63939b64a4c750d16f5c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:06:42.273227Z","signature_b64":"vOqlzXoAZmiDIUNni0f+Sw1kpWMlxJhoM2AbkI/+mqQwkOI8YUZOlAzxWQBOiYt7geNyDhqjRwKANdsE3O8kAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea39a613eb2f09e90c97d336a9d388406684099f6d851f74c9e3c983f744aecb","last_reissued_at":"2026-05-18T03:06:42.272593Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:06:42.272593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analyzing Evolutionary Optimization in Noisy Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AI","authors_text":"Chao Qian, Yang Yu, Zhi-Hua Zhou","submitted_at":"2013-11-20T09:28:52Z","abstract_excerpt":"Many optimization tasks have to be handled in noisy environments, where we cannot obtain the exact evaluation of a solution but only a noisy one. For noisy optimization tasks, evolutionary algorithms (EAs), a kind of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on empirical studying and designing EAs for noisy optimization, while, the theoretical counterpart has been little investigated. In this paper, we investigate a largely ignored question, i.e., whether an optimization problem will always become harder for EAs in a nois"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.4987","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":"1311.4987","created_at":"2026-05-18T03:06:42.272726+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.4987v1","created_at":"2026-05-18T03:06:42.272726+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.4987","created_at":"2026-05-18T03:06:42.272726+00:00"},{"alias_kind":"pith_short_12","alias_value":"5I42ME7LF4E6","created_at":"2026-05-18T12:27:34.582898+00:00"},{"alias_kind":"pith_short_16","alias_value":"5I42ME7LF4E6SDEX","created_at":"2026-05-18T12:27:34.582898+00:00"},{"alias_kind":"pith_short_8","alias_value":"5I42ME7L","created_at":"2026-05-18T12:27:34.582898+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/5I42ME7LF4E6SDEX2M3KTU4IIB","json":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB.json","graph_json":"https://pith.science/api/pith-number/5I42ME7LF4E6SDEX2M3KTU4IIB/graph.json","events_json":"https://pith.science/api/pith-number/5I42ME7LF4E6SDEX2M3KTU4IIB/events.json","paper":"https://pith.science/paper/5I42ME7L"},"agent_actions":{"view_html":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB","download_json":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB.json","view_paper":"https://pith.science/paper/5I42ME7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.4987&json=true","fetch_graph":"https://pith.science/api/pith-number/5I42ME7LF4E6SDEX2M3KTU4IIB/graph.json","fetch_events":"https://pith.science/api/pith-number/5I42ME7LF4E6SDEX2M3KTU4IIB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB/action/storage_attestation","attest_author":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB/action/author_attestation","sign_citation":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB/action/citation_signature","submit_replication":"https://pith.science/pith/5I42ME7LF4E6SDEX2M3KTU4IIB/action/replication_record"}},"created_at":"2026-05-18T03:06:42.272726+00:00","updated_at":"2026-05-18T03:06:42.272726+00:00"}