{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:S6NHB72RZAUERO4TXUVTAP67JG","short_pith_number":"pith:S6NHB72R","canonical_record":{"source":{"id":"2009.06390","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-10T18:47:04Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"833fcf81351f3f35be796906993f7683446c1177d430622b3d7ba829f8801f46","abstract_canon_sha256":"f4ce95215e0c126556310ab9f06a20fabe209d5607536208bed235456095cdb0"},"schema_version":"1.0"},"canonical_sha256":"979a70ff51c82848bb93bd2b303fdf498448244ce91a754a2b12fa4adb933983","source":{"kind":"arxiv","id":"2009.06390","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.06390","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"arxiv_version","alias_value":"2009.06390v1","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.06390","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"pith_short_12","alias_value":"S6NHB72RZAUE","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"pith_short_16","alias_value":"S6NHB72RZAUERO4T","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"pith_short_8","alias_value":"S6NHB72R","created_at":"2026-07-05T01:35:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:S6NHB72RZAUERO4TXUVTAP67JG","target":"record","payload":{"canonical_record":{"source":{"id":"2009.06390","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-10T18:47:04Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"833fcf81351f3f35be796906993f7683446c1177d430622b3d7ba829f8801f46","abstract_canon_sha256":"f4ce95215e0c126556310ab9f06a20fabe209d5607536208bed235456095cdb0"},"schema_version":"1.0"},"canonical_sha256":"979a70ff51c82848bb93bd2b303fdf498448244ce91a754a2b12fa4adb933983","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:35:08.081730Z","signature_b64":"fT80ouM8c1pZBE9cmNXR9NBAg/4ca5Elam5WiMCN+vBPjWLrryIxIrOYd7AN7Ab4pBw0k/ZjoxH7Gw3Ya3+NCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"979a70ff51c82848bb93bd2b303fdf498448244ce91a754a2b12fa4adb933983","last_reissued_at":"2026-07-05T01:35:08.081386Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:35:08.081386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.06390","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:35:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mthJu5opMjCuHz+WLXyhw2673pumf+NeKwAUyABvd7WawioFzCXo5ZMk8zcjZPFv9t2fT+f8mpSU8pxfj7CQDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:26:03.958614Z"},"content_sha256":"975a587d7e0ffd632ad6d766193b8bdcf20c03099bc8c6c5ffc7f9fc7aee18cb","schema_version":"1.0","event_id":"sha256:975a587d7e0ffd632ad6d766193b8bdcf20c03099bc8c6c5ffc7f9fc7aee18cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:S6NHB72RZAUERO4TXUVTAP67JG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Baowen Xu, Fan Wu, Leslie Kanthan, Lingbo Li, Michail Basios, Yuxi Huan","submitted_at":"2020-09-10T18:47:04Z","abstract_excerpt":"Hyperparameter optimisation is a crucial process in searching the optimal machine learning model. The efficiency of finding the optimal hyperparameter settings has been a big concern in recent researches since the optimisation process could be time-consuming, especially when the objective functions are highly expensive to evaluate. In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification probl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.06390","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2009.06390/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:35:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ofl+69i5sRH1qnAw5yozHdsclZ0RNKIAoGjBZNZdkl3hHE0TBDe0XQf58W5JeMePZSdRU94uf91bXmo8EM+xBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:26:03.959595Z"},"content_sha256":"02b24f148a00c268ec1c429bc104827aed138eed1aa0d68d31826a643e0c8130","schema_version":"1.0","event_id":"sha256:02b24f148a00c268ec1c429bc104827aed138eed1aa0d68d31826a643e0c8130"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S6NHB72RZAUERO4TXUVTAP67JG/bundle.json","state_url":"https://pith.science/pith/S6NHB72RZAUERO4TXUVTAP67JG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S6NHB72RZAUERO4TXUVTAP67JG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T06:26:03Z","links":{"resolver":"https://pith.science/pith/S6NHB72RZAUERO4TXUVTAP67JG","bundle":"https://pith.science/pith/S6NHB72RZAUERO4TXUVTAP67JG/bundle.json","state":"https://pith.science/pith/S6NHB72RZAUERO4TXUVTAP67JG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S6NHB72RZAUERO4TXUVTAP67JG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:S6NHB72RZAUERO4TXUVTAP67JG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f4ce95215e0c126556310ab9f06a20fabe209d5607536208bed235456095cdb0","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-10T18:47:04Z","title_canon_sha256":"833fcf81351f3f35be796906993f7683446c1177d430622b3d7ba829f8801f46"},"schema_version":"1.0","source":{"id":"2009.06390","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.06390","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"arxiv_version","alias_value":"2009.06390v1","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.06390","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"pith_short_12","alias_value":"S6NHB72RZAUE","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"pith_short_16","alias_value":"S6NHB72RZAUERO4T","created_at":"2026-07-05T01:35:08Z"},{"alias_kind":"pith_short_8","alias_value":"S6NHB72R","created_at":"2026-07-05T01:35:08Z"}],"graph_snapshots":[{"event_id":"sha256:02b24f148a00c268ec1c429bc104827aed138eed1aa0d68d31826a643e0c8130","target":"graph","created_at":"2026-07-05T01:35:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2009.06390/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperparameter optimisation is a crucial process in searching the optimal machine learning model. The efficiency of finding the optimal hyperparameter settings has been a big concern in recent researches since the optimisation process could be time-consuming, especially when the objective functions are highly expensive to evaluate. In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification probl","authors_text":"Baowen Xu, Fan Wu, Leslie Kanthan, Lingbo Li, Michail Basios, Yuxi Huan","cross_cats":["cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-10T18:47:04Z","title":"IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.06390","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:975a587d7e0ffd632ad6d766193b8bdcf20c03099bc8c6c5ffc7f9fc7aee18cb","target":"record","created_at":"2026-07-05T01:35:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f4ce95215e0c126556310ab9f06a20fabe209d5607536208bed235456095cdb0","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-10T18:47:04Z","title_canon_sha256":"833fcf81351f3f35be796906993f7683446c1177d430622b3d7ba829f8801f46"},"schema_version":"1.0","source":{"id":"2009.06390","kind":"arxiv","version":1}},"canonical_sha256":"979a70ff51c82848bb93bd2b303fdf498448244ce91a754a2b12fa4adb933983","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"979a70ff51c82848bb93bd2b303fdf498448244ce91a754a2b12fa4adb933983","first_computed_at":"2026-07-05T01:35:08.081386Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:35:08.081386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fT80ouM8c1pZBE9cmNXR9NBAg/4ca5Elam5WiMCN+vBPjWLrryIxIrOYd7AN7Ab4pBw0k/ZjoxH7Gw3Ya3+NCA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:35:08.081730Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.06390","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:975a587d7e0ffd632ad6d766193b8bdcf20c03099bc8c6c5ffc7f9fc7aee18cb","sha256:02b24f148a00c268ec1c429bc104827aed138eed1aa0d68d31826a643e0c8130"],"state_sha256":"a62b6f0c81d7bac267e1d304e3c388f572cba57e64240d323db620190d68852d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1tikRjp9cNGc9OA/21Q53UP3/nUp/zGxF3MW3g9ryjr7o95pxMogvwHFjk0tP46IgVIMmdXVle6OKr1hKNMkCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T06:26:03.965670Z","bundle_sha256":"169b082e93c65c343dc3094978009de3cca3ad74df243026ec52e75ea9b1474c"}}