{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:EQNBZCP432JNUJMRACEBPFXIGO","short_pith_number":"pith:EQNBZCP4","schema_version":"1.0","canonical_sha256":"241a1c89fcde92da259100881796e833801530338f7d44322fae766caa49313b","source":{"kind":"arxiv","id":"1901.00577","version":1},"attestation_state":"computed","paper":{"title":"An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Chong Zhao, Guizeng You, Mengfei Dou, Xinian Guo, Xinwu Yang","submitted_at":"2019-01-03T02:12:07Z","abstract_excerpt":"Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal"},"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":"1901.00577","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-03T02:12:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7b0a47163ff0e77b892a53840d4fcd9c58fed19992e8136c450808c394b07629","abstract_canon_sha256":"d9935da78cd347f0a599ae15ee3146574dd10d6584c0afee2cfba19400782ffe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:02.091275Z","signature_b64":"E3Un8RIplptivXbIJrENCEgjRlSuqc8BQz5/pY+wXGQDMYoPKcKNpqJEYoqKOo20VdoOQrw+xlgAbVJ8hKC+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"241a1c89fcde92da259100881796e833801530338f7d44322fae766caa49313b","last_reissued_at":"2026-05-17T23:57:02.090609Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:02.090609Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Chong Zhao, Guizeng You, Mengfei Dou, Xinian Guo, Xinwu Yang","submitted_at":"2019-01-03T02:12:07Z","abstract_excerpt":"Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.00577","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":"1901.00577","created_at":"2026-05-17T23:57:02.090703+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.00577v1","created_at":"2026-05-17T23:57:02.090703+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.00577","created_at":"2026-05-17T23:57:02.090703+00:00"},{"alias_kind":"pith_short_12","alias_value":"EQNBZCP432JN","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"EQNBZCP432JNUJMR","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"EQNBZCP4","created_at":"2026-05-18T12:33:15.570797+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/EQNBZCP432JNUJMRACEBPFXIGO","json":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO.json","graph_json":"https://pith.science/api/pith-number/EQNBZCP432JNUJMRACEBPFXIGO/graph.json","events_json":"https://pith.science/api/pith-number/EQNBZCP432JNUJMRACEBPFXIGO/events.json","paper":"https://pith.science/paper/EQNBZCP4"},"agent_actions":{"view_html":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO","download_json":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO.json","view_paper":"https://pith.science/paper/EQNBZCP4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.00577&json=true","fetch_graph":"https://pith.science/api/pith-number/EQNBZCP432JNUJMRACEBPFXIGO/graph.json","fetch_events":"https://pith.science/api/pith-number/EQNBZCP432JNUJMRACEBPFXIGO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO/action/storage_attestation","attest_author":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO/action/author_attestation","sign_citation":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO/action/citation_signature","submit_replication":"https://pith.science/pith/EQNBZCP432JNUJMRACEBPFXIGO/action/replication_record"}},"created_at":"2026-05-17T23:57:02.090703+00:00","updated_at":"2026-05-17T23:57:02.090703+00:00"}