{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:4H22DHCWZ2S2NRCEU6IXV5KMYR","short_pith_number":"pith:4H22DHCW","schema_version":"1.0","canonical_sha256":"e1f5a19c56cea5a6c444a7917af54cc46ce1f339b88ae779950d9df44a25bf5c","source":{"kind":"arxiv","id":"1511.05625","version":1},"attestation_state":"computed","paper":{"title":"MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Alexander Mendiburu, Aurora Trinidad Ramirez Pozo, Murilo Zangari de Souza, Roberto Santana","submitted_at":"2015-11-18T00:04:35Z","abstract_excerpt":"Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems. These algorithms learn and sample probabilistic graphical models able to encode and exploit the regularities of the problem. This paper investigates the effect of using probabilistic modeling techniques as a way to enhance the behavior of MOEA/D framework. MOEA/D is a decomposition based evolutionary algorithm that decomposes a multi-objective optimization problem (MOP) in a number of scalar single-objective subproblems and op"},"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":"1511.05625","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2015-11-18T00:04:35Z","cross_cats_sorted":[],"title_canon_sha256":"3e642217da900cb999dfcd517ffec6982e52c344a11383b3eec62f30d259a39e","abstract_canon_sha256":"be9c6d96bfc0a539d8181becea5b22afff144f2c5b9938dca8de3c8710372c2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:33.220109Z","signature_b64":"2qnZXXo+0gWQ+pDXBoSP/IsikqJHjMkZleJG58z7lSSo46ReJy9xf55g+w852vWgj+eMsjjhf5KXDW1ZO2kWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1f5a19c56cea5a6c444a7917af54cc46ce1f339b88ae779950d9df44a25bf5c","last_reissued_at":"2026-05-18T01:26:33.219380Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:33.219380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Alexander Mendiburu, Aurora Trinidad Ramirez Pozo, Murilo Zangari de Souza, Roberto Santana","submitted_at":"2015-11-18T00:04:35Z","abstract_excerpt":"Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems. These algorithms learn and sample probabilistic graphical models able to encode and exploit the regularities of the problem. This paper investigates the effect of using probabilistic modeling techniques as a way to enhance the behavior of MOEA/D framework. MOEA/D is a decomposition based evolutionary algorithm that decomposes a multi-objective optimization problem (MOP) in a number of scalar single-objective subproblems and op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05625","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":"1511.05625","created_at":"2026-05-18T01:26:33.219486+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.05625v1","created_at":"2026-05-18T01:26:33.219486+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05625","created_at":"2026-05-18T01:26:33.219486+00:00"},{"alias_kind":"pith_short_12","alias_value":"4H22DHCWZ2S2","created_at":"2026-05-18T12:29:05.191682+00:00"},{"alias_kind":"pith_short_16","alias_value":"4H22DHCWZ2S2NRCE","created_at":"2026-05-18T12:29:05.191682+00:00"},{"alias_kind":"pith_short_8","alias_value":"4H22DHCW","created_at":"2026-05-18T12:29:05.191682+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/4H22DHCWZ2S2NRCEU6IXV5KMYR","json":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR.json","graph_json":"https://pith.science/api/pith-number/4H22DHCWZ2S2NRCEU6IXV5KMYR/graph.json","events_json":"https://pith.science/api/pith-number/4H22DHCWZ2S2NRCEU6IXV5KMYR/events.json","paper":"https://pith.science/paper/4H22DHCW"},"agent_actions":{"view_html":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR","download_json":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR.json","view_paper":"https://pith.science/paper/4H22DHCW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.05625&json=true","fetch_graph":"https://pith.science/api/pith-number/4H22DHCWZ2S2NRCEU6IXV5KMYR/graph.json","fetch_events":"https://pith.science/api/pith-number/4H22DHCWZ2S2NRCEU6IXV5KMYR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR/action/storage_attestation","attest_author":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR/action/author_attestation","sign_citation":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR/action/citation_signature","submit_replication":"https://pith.science/pith/4H22DHCWZ2S2NRCEU6IXV5KMYR/action/replication_record"}},"created_at":"2026-05-18T01:26:33.219486+00:00","updated_at":"2026-05-18T01:26:33.219486+00:00"}