{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:A7PXPCOVIJGEJ6O3IPZXHKB2OH","short_pith_number":"pith:A7PXPCOV","schema_version":"1.0","canonical_sha256":"07df7789d5424c44f9db43f373a83a71eb1c21f15d51237411673020f73fe606","source":{"kind":"arxiv","id":"1504.05619","version":1},"attestation_state":"computed","paper":{"title":"Learning Opposites with Evolving Rules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Hamid R. Tizhoosh, Shahryar Rahnamayan","submitted_at":"2015-04-21T22:16:17Z","abstract_excerpt":"The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their opposition-based version to become faster and/or more accurate. However, most works still use a simple notion of opposites, namely linear (or type- I) opposition, that for each $x\\in[a,b]$ assigns its opposite as $\\breve{x}_I=a+b-x$. This, of course, is a very naive est"},"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":"1504.05619","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2015-04-21T22:16:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c77f2db2e015be1cf40843e2f0fe8dab56ce6bea6de51b61366106b5f2c4e009","abstract_canon_sha256":"5a2cc6d39497cf89a9b4967da94eb71c429b86f0c57bf895b7e97431650fd496"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:13.135523Z","signature_b64":"Hl/EtOFWawZ8/9QBZ80mx6w3WNr5Qrdm95eAOk/QvE68beuBdf2wxVNuHjnq00p+Y6MXxrTqzqA2zTZViYMpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07df7789d5424c44f9db43f373a83a71eb1c21f15d51237411673020f73fe606","last_reissued_at":"2026-05-18T02:18:13.134923Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:13.134923Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Opposites with Evolving Rules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Hamid R. Tizhoosh, Shahryar Rahnamayan","submitted_at":"2015-04-21T22:16:17Z","abstract_excerpt":"The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their opposition-based version to become faster and/or more accurate. However, most works still use a simple notion of opposites, namely linear (or type- I) opposition, that for each $x\\in[a,b]$ assigns its opposite as $\\breve{x}_I=a+b-x$. This, of course, is a very naive est"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.05619","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":"1504.05619","created_at":"2026-05-18T02:18:13.135015+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.05619v1","created_at":"2026-05-18T02:18:13.135015+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.05619","created_at":"2026-05-18T02:18:13.135015+00:00"},{"alias_kind":"pith_short_12","alias_value":"A7PXPCOVIJGE","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_16","alias_value":"A7PXPCOVIJGEJ6O3","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_8","alias_value":"A7PXPCOV","created_at":"2026-05-18T12:29:10.953037+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/A7PXPCOVIJGEJ6O3IPZXHKB2OH","json":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH.json","graph_json":"https://pith.science/api/pith-number/A7PXPCOVIJGEJ6O3IPZXHKB2OH/graph.json","events_json":"https://pith.science/api/pith-number/A7PXPCOVIJGEJ6O3IPZXHKB2OH/events.json","paper":"https://pith.science/paper/A7PXPCOV"},"agent_actions":{"view_html":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH","download_json":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH.json","view_paper":"https://pith.science/paper/A7PXPCOV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.05619&json=true","fetch_graph":"https://pith.science/api/pith-number/A7PXPCOVIJGEJ6O3IPZXHKB2OH/graph.json","fetch_events":"https://pith.science/api/pith-number/A7PXPCOVIJGEJ6O3IPZXHKB2OH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH/action/storage_attestation","attest_author":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH/action/author_attestation","sign_citation":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH/action/citation_signature","submit_replication":"https://pith.science/pith/A7PXPCOVIJGEJ6O3IPZXHKB2OH/action/replication_record"}},"created_at":"2026-05-18T02:18:13.135015+00:00","updated_at":"2026-05-18T02:18:13.135015+00:00"}