{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:G5T7FXI5ZK6C5KLCQDTPWNOHJ6","short_pith_number":"pith:G5T7FXI5","schema_version":"1.0","canonical_sha256":"3767f2dd1dcabc2ea96280e6fb35c74f9a1ddcac04ab4edef33dbb40513816d5","source":{"kind":"arxiv","id":"1003.4314","version":1},"attestation_state":"computed","paper":{"title":"A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Daniel Karapetyan, Gregory Gutin","submitted_at":"2010-03-23T00:27:13Z","abstract_excerpt":"Memetic Algorithms are known to be a powerful technique in solving hard optimization problems.  To design a memetic algorithm one needs to make a host of decisions; selecting a population size is one of the most important among them.  Most algorithms in the literature fix the population size to a certain constant value.  This reduces the algorithm's quality since the optimal population size varies for different instances, local search procedures and running times.  In this paper we propose an adjustable population size.  It is calculated as a function of the running time of the whole algorithm"},"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":"1003.4314","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2010-03-23T00:27:13Z","cross_cats_sorted":[],"title_canon_sha256":"79b5065331c64de341617572251eb508a87face295a6dc0bfc382d69e4ada703","abstract_canon_sha256":"beba3e1252167d4f19b4d2704d44177662196978128a664471eb9422f9006efd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:24:36.228445Z","signature_b64":"6/lqEcKbxnyWprA3AO1fM8vXe52Bq1e4h3gj+D0/tiVoh/54gx4xDaEyHMh7pKFoHuuLZkRSHgGGIHhGI3B/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3767f2dd1dcabc2ea96280e6fb35c74f9a1ddcac04ab4edef33dbb40513816d5","last_reissued_at":"2026-05-18T02:24:36.227678Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:24:36.227678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Daniel Karapetyan, Gregory Gutin","submitted_at":"2010-03-23T00:27:13Z","abstract_excerpt":"Memetic Algorithms are known to be a powerful technique in solving hard optimization problems.  To design a memetic algorithm one needs to make a host of decisions; selecting a population size is one of the most important among them.  Most algorithms in the literature fix the population size to a certain constant value.  This reduces the algorithm's quality since the optimal population size varies for different instances, local search procedures and running times.  In this paper we propose an adjustable population size.  It is calculated as a function of the running time of the whole algorithm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1003.4314","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":"1003.4314","created_at":"2026-05-18T02:24:36.227797+00:00"},{"alias_kind":"arxiv_version","alias_value":"1003.4314v1","created_at":"2026-05-18T02:24:36.227797+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1003.4314","created_at":"2026-05-18T02:24:36.227797+00:00"},{"alias_kind":"pith_short_12","alias_value":"G5T7FXI5ZK6C","created_at":"2026-05-18T12:26:07.630475+00:00"},{"alias_kind":"pith_short_16","alias_value":"G5T7FXI5ZK6C5KLC","created_at":"2026-05-18T12:26:07.630475+00:00"},{"alias_kind":"pith_short_8","alias_value":"G5T7FXI5","created_at":"2026-05-18T12:26:07.630475+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/G5T7FXI5ZK6C5KLCQDTPWNOHJ6","json":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6.json","graph_json":"https://pith.science/api/pith-number/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/graph.json","events_json":"https://pith.science/api/pith-number/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/events.json","paper":"https://pith.science/paper/G5T7FXI5"},"agent_actions":{"view_html":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6","download_json":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6.json","view_paper":"https://pith.science/paper/G5T7FXI5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1003.4314&json=true","fetch_graph":"https://pith.science/api/pith-number/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/graph.json","fetch_events":"https://pith.science/api/pith-number/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/action/storage_attestation","attest_author":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/action/author_attestation","sign_citation":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/action/citation_signature","submit_replication":"https://pith.science/pith/G5T7FXI5ZK6C5KLCQDTPWNOHJ6/action/replication_record"}},"created_at":"2026-05-18T02:24:36.227797+00:00","updated_at":"2026-05-18T02:24:36.227797+00:00"}