{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:OXVXI4RLVDDJQK4XUXYIL7D7HW","short_pith_number":"pith:OXVXI4RL","schema_version":"1.0","canonical_sha256":"75eb74722ba8c6982b97a5f085fc7f3d97fe950bd3bac5323e784fa1cca0d880","source":{"kind":"arxiv","id":"1304.3200","version":1},"attestation_state":"computed","paper":{"title":"An Approach to Solve Linear Equations Using a Time-Variant Adaptation Based Hybrid Evolutionary Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"cs.NE","authors_text":"A.R.M. Jalal Uddin Jamali, Md. Bazlar Rahman, M.M.A. Hashem","submitted_at":"2013-04-11T05:36:53Z","abstract_excerpt":"For small number of equations, systems of linear (and sometimes nonlinear) equations can be solved by simple classical techniques. However, for large number of systems of linear (or nonlinear) equations, solutions using classical method become arduous. On the other hand evolutionary algorithms have mostly been used to solve various optimization and learning problems. Recently, hybridization of evolutionary algorithm with classical Gauss-Seidel based Successive Over Relaxation (SOR) method has successfully been used to solve large number of linear equations; where a uniform adaptation (UA) tech"},"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":"1304.3200","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2013-04-11T05:36:53Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"0eee66ff002b0dcce275aa462c7ce17794f7dbe2770c3767f7c9084fd78ee439","abstract_canon_sha256":"f1a4c284758d09769d8d6ec4a4d66d889219a06a7b4260eb9c269c3d06b87ca4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:28:04.640785Z","signature_b64":"IHAxGo7La7OYiYpr+zPA5kT5PRBb7j2Zc9wctpmwSDwk58Syzy1Gb3m8Qk5KAk7o7Pr4dF0E/TWjgenRi90wDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75eb74722ba8c6982b97a5f085fc7f3d97fe950bd3bac5323e784fa1cca0d880","last_reissued_at":"2026-05-18T03:28:04.640066Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:28:04.640066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Approach to Solve Linear Equations Using a Time-Variant Adaptation Based Hybrid Evolutionary Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"cs.NE","authors_text":"A.R.M. Jalal Uddin Jamali, Md. Bazlar Rahman, M.M.A. Hashem","submitted_at":"2013-04-11T05:36:53Z","abstract_excerpt":"For small number of equations, systems of linear (and sometimes nonlinear) equations can be solved by simple classical techniques. However, for large number of systems of linear (or nonlinear) equations, solutions using classical method become arduous. On the other hand evolutionary algorithms have mostly been used to solve various optimization and learning problems. Recently, hybridization of evolutionary algorithm with classical Gauss-Seidel based Successive Over Relaxation (SOR) method has successfully been used to solve large number of linear equations; where a uniform adaptation (UA) tech"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1304.3200","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":"1304.3200","created_at":"2026-05-18T03:28:04.640188+00:00"},{"alias_kind":"arxiv_version","alias_value":"1304.3200v1","created_at":"2026-05-18T03:28:04.640188+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1304.3200","created_at":"2026-05-18T03:28:04.640188+00:00"},{"alias_kind":"pith_short_12","alias_value":"OXVXI4RLVDDJ","created_at":"2026-05-18T12:27:54.935989+00:00"},{"alias_kind":"pith_short_16","alias_value":"OXVXI4RLVDDJQK4X","created_at":"2026-05-18T12:27:54.935989+00:00"},{"alias_kind":"pith_short_8","alias_value":"OXVXI4RL","created_at":"2026-05-18T12:27:54.935989+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/OXVXI4RLVDDJQK4XUXYIL7D7HW","json":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW.json","graph_json":"https://pith.science/api/pith-number/OXVXI4RLVDDJQK4XUXYIL7D7HW/graph.json","events_json":"https://pith.science/api/pith-number/OXVXI4RLVDDJQK4XUXYIL7D7HW/events.json","paper":"https://pith.science/paper/OXVXI4RL"},"agent_actions":{"view_html":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW","download_json":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW.json","view_paper":"https://pith.science/paper/OXVXI4RL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1304.3200&json=true","fetch_graph":"https://pith.science/api/pith-number/OXVXI4RLVDDJQK4XUXYIL7D7HW/graph.json","fetch_events":"https://pith.science/api/pith-number/OXVXI4RLVDDJQK4XUXYIL7D7HW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW/action/storage_attestation","attest_author":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW/action/author_attestation","sign_citation":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW/action/citation_signature","submit_replication":"https://pith.science/pith/OXVXI4RLVDDJQK4XUXYIL7D7HW/action/replication_record"}},"created_at":"2026-05-18T03:28:04.640188+00:00","updated_at":"2026-05-18T03:28:04.640188+00:00"}