{"as_of":"2026-08-17T08:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:75035eecb722b4e99414f89cece02e26d59e68de1f1a3c4b45e7e891422a0716","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:59:37.695086Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:38:47.169264Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-14T11:05:34.312969Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.08006","snapshot_observed_at":"2026-08-14T11:38:47.169264Z","title":null,"venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"1908.08563","last_updated":"2019-08-22T19:01:09Z","snapshot_observed_at":"2026-08-16T06:28:28.185056Z","submitted_at":"2019-08-22T19:01:09Z","title":"Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:47.169264Z"},"links":{"cited_paper":"/paper/1908.08006","citing_paper":"/paper/1908.08563"},"observation_digest":"sha256:ba08184e05ff2023efc359eff0407e4c87a97256e26fc74e192845a150f714ef","observation_id":"fbc00699-82e7-4633-8e27-2210787d7fed","resolution":{"observed_at":"2026-08-14T11:38:47.169264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"cited_work":{"arxiv_id":"1908.08006","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.08006","snapshot_observed_at":"2026-08-14T11:05:34.312969Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","venue":"cs.NE","work_id":"01f7dac6-2603-45f8-aff5-fc692f4fc928","year":2019},"citing_paper":{"arxiv_id":"1908.09788","last_updated":"2019-08-26T16:42:33Z","snapshot_observed_at":"2026-08-16T15:54:56.933655Z","submitted_at":"2019-08-26T16:42:33Z","title":"An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T11:05:33.983532Z"},"links":{"cited_paper":"/paper/1908.08006","citing_paper":"/paper/1908.09788"},"observation_digest":"sha256:030b8a58d7efc95a4d88282193f1021360a7a0c9754d653df9acccc48b024a32","observation_id":"5f971ee3-cbe1-4285-8db0-1845996a689a","resolution":{"observed_at":"2026-08-14T11:05:34.317297Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1908.08006/citation-record","integrity":"/paper/1908.08006/integrity","json":"/paper/1908.08006/citation-record.json","paper":"/paper/1908.08006"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.495222Z","title":null,"venue":null,"work_id":"af52c5a2-5d7d-44ce-a55d-33a01d51ee6e","year":2000},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.437376Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:cdf72d1e32cc7b035808530e6d7458764fcf5d1611574e432086a10cb23f2f67","observation_id":"11397b4a-a5ca-4ce8-8077-fd9ebe3d9172","resolution":{"observed_at":"2026-08-14T12:59:38.499727Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.12914","last_updated":"2020-10-19T16:10:16Z","snapshot_observed_at":"2026-08-16T17:48:00.329314Z","submitted_at":"2019-07-23T16:13:53Z","title":"Evolutionary Algorithms and Efficient Data Analytics for Image Processing","version":3},"cited_work":{"arxiv_id":"1907.12914","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.12914","snapshot_observed_at":"2026-08-14T12:59:37.756890Z","title":"Evolutionary Algorithms and Efficient Data Analytics for Image Processing","venue":"cs.CV","work_id":"ab24fea7-ae81-4544-a17d-aee2f27fb2b4","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.441956Z"},"links":{"cited_paper":"/paper/1907.12914","citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:5096d1f5a660af4370df8420e86afce8873e2dc7d680e4b012418bff3c63c37a","observation_id":"6112a43c-bd84-4f33-a604-bf7e00626cfb","resolution":{"observed_at":"2026-08-14T12:59:37.760704Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.10230","last_updated":"2019-06-10T18:19:33Z","snapshot_observed_at":"2026-08-14T18:58:49.794741Z","submitted_at":"2018-06-26T22:14:36Z","title":"Guided evolutionary strategies: Augmenting random search with surrogate gradients","version":4},"cited_work":{"arxiv_id":"1806.10230","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.10230","snapshot_observed_at":"2026-08-14T12:59:37.739754Z","title":"Guided evolutionary strategies: Augmenting random search with surrogate gradients","venue":"cs.NE","work_id":"78b8a2b9-ed1d-4fb8-8fd3-6f6028319e4a","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.447658Z"},"links":{"cited_paper":"/paper/1806.10230","citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:e92255c89edf8c78fa6b12fe57efe26c34963267a6aad5a892072af868d3c979","observation_id":"f6c7286f-a5ca-4f4c-90a5-ea824ca09598","resolution":{"observed_at":"2026-08-14T12:59:37.745317Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.482873Z","title":null,"venue":null,"work_id":"c34eead8-c1ec-4ec7-90c2-b4bccf8e994a","year":2016},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.452245Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:a10a100e4a450aaf2a0dc625a751fc2bf004b962470a5607a170b2e0973a9f46","observation_id":"c1ea17c9-a318-4829-965b-c5bb0506a86a","resolution":{"observed_at":"2026-08-14T12:59:38.487409Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.471421Z","title":"Supervised representation learning: Transfer learning with deep autoencoders","venue":null,"work_id":"41229d0e-e4c1-4a1f-abac-a5a20dbff638","year":2015},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.456363Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:ab81991907096b24db95691a60577f7250bea47995dc78f6794ee00708c25b90","observation_id":"379c96fb-70df-4ec0-a6e2-a7eaebfc4990","resolution":{"observed_at":"2026-08-14T12:59:38.475563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.458209Z","title":"Semi-supervised learning for discrete choice models","venue":null,"work_id":"8051f227-d892-4e85-a425-84402535ee62","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.460935Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:26dc50d4ba1e9220366c5917fdbcfcab96dd7caeb323214922be5385c53114a2","observation_id":"31c71093-958a-49a0-8293-8084ef332c47","resolution":{"observed_at":"2026-08-14T12:59:38.463028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.445708Z","title":"The curse (s) of dimensionality","venue":null,"work_id":"d80874c9-c92e-44f5-8c11-19011ea822f8","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.465156Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:dc5163f5ff0a451c1631bd7258d0d08f6feec689e4b04607da48c281ac3de5dd","observation_id":"35ad2471-4fff-4356-98a9-e30b9b436428","resolution":{"observed_at":"2026-08-14T12:59:38.449992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.00258","last_updated":"2018-12-01T20:23:19Z","snapshot_observed_at":"2026-08-14T17:50:07.075336Z","submitted_at":"2018-12-01T20:23:19Z","title":"A New Approach for Large Scale Multiple Testing with Application to FDR Control for Graphically Structured Hypotheses","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.00258","snapshot_observed_at":"2026-08-14T12:59:37.470012Z","title":"A new approach for large scale mul- tiple testing with application to fdr control for graphically structured hypotheses","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.470012Z"},"links":{"cited_paper":"/paper/1812.00258","citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:19b9dacd497cdbabdcae0517b17b1244a807a8441788f3f8ec68c030bbc0644d","observation_id":"f3a391c4-5a8a-4924-8a70-ff286a64eb8c","resolution":{"observed_at":"2026-08-14T12:59:37.470012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.433126Z","title":"A reminiscent study of nature inspired computation","venue":null,"work_id":"e9480c1e-36f3-4eb3-be39-fdd2453f9d71","year":2011},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.474081Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:0df912803afe25e5b1f1eabcf930ea0c22a80f88224dc0f8c9046f5f32ec3860","observation_id":"3cfe4122-06c6-4420-8c0e-613c956829c6","resolution":{"observed_at":"2026-08-14T12:59:38.437110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.421699Z","title":"Stellar-mass black hole optimization for biclustering microarray gene expression data.Applied Artiﬁcial Intelligence, 29(4):353– 381, 2015","venue":null,"work_id":"40026ccf-3997-4c67-bf43-944ce4b38650","year":2015},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.478227Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:ef16ca486b8841680997354ddcef093d799fa0fde6cdad467d4e87527756224d","observation_id":"6394dcb5-b7bc-45b1-8308-cb0c362039d9","resolution":{"observed_at":"2026-08-14T12:59:38.425460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.410716Z","title":"Metaheuristics in combinatorial optimization: Overview and conceptual comparison","venue":null,"work_id":"b0757a88-1511-453c-b94c-6d16f48bb76f","year":2003},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.482112Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:4e7f7a255aafb5d9a0b313319348b34bb9a8b4529965bef00dff7f6da8534f01","observation_id":"e55095d7-f583-4432-b18e-698f9da4aaa8","resolution":{"observed_at":"2026-08-14T12:59:38.414350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.399028Z","title":"Evolutionary computation 1: Basic algorithms and operators","venue":null,"work_id":"60aba9fd-0638-44a5-99f3-8db90bd9ed78","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.485410Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:4c96c55d1951bc99be874d30aeb3b376e2162b659b0b0bf60406478c716aea21","observation_id":"cf0a49cc-1583-4458-a9c0-bcddf04b71ab","resolution":{"observed_at":"2026-08-14T12:59:38.403214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.384756Z","title":"Coyote optimization algorithm: a new metaheuristic for global optimization problems","venue":null,"work_id":"aacbfa71-e146-43a1-8804-2e1aabdbbf6e","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.489472Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:83cb45ab50f4875d471073ae6ebbeee94112e0c621d0f070ec5377cfe4b9440d","observation_id":"0dd20401-952d-43c7-9d19-792ed7ecb9e8","resolution":{"observed_at":"2026-08-14T12:59:38.389221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.492949Z","title":"Image steganalysis using a bee colony based feature selection algorithm","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.492949Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:fe71896286a9564f4860552ea8a165c53c73ef7c9529f98b351033637c229e19","observation_id":"82518480-fe57-4116-8c30-c26827d5dab4","resolution":{"observed_at":"2026-08-14T12:59:37.492949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.363962Z","title":"A new metaheuristic feature subset selection approach for image steganalysis","venue":null,"work_id":"a7f86595-2e45-41a0-abe5-381c6f59024b","year":2014},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.496062Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:7614cd143aa665d7a8d26f6e3d274dcd822b55f5d3b2195b09911ef162c96c6b","observation_id":"b704e43d-2062-4121-9862-d7f931e402f9","resolution":{"observed_at":"2026-08-14T12:59:38.368139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.352015Z","title":"Uncertainty quan- tiﬁcation in an engineering design software system","venue":null,"work_id":"05df9e29-c3c1-4477-837c-64a665458a38","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.499314Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:563c0f7cbdde91d38df3f7c3557a25d20068533355d0770084e0fabce7a34746","observation_id":"70dfe36f-dc05-40a9-ba76-aedffc83ca69","resolution":{"observed_at":"2026-08-14T12:59:38.356313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.340158Z","title":"Precision medicine and the cursed dimensions","venue":null,"work_id":"79e70351-2cf3-4b73-a973-43b6f7bd6168","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.503018Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:ba6a90e60b96413f9551f68fd68052d4b8667fcb69398b1fcb9214fd57b02aa5","observation_id":"6b460794-0e3e-4794-86f2-7a79b4c25082","resolution":{"observed_at":"2026-08-14T12:59:38.344356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.328537Z","title":"Automated diagnosis system for alzheimer disease using features selected by artiﬁcial bee colony","venue":null,"work_id":"736bcc4b-9867-4f59-95fb-b425c75cc9c1","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.506760Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:2418f50d0667992bd227c0a415b07355730076f5be58587a156715856fd9a6a1","observation_id":"565807f7-8913-480b-aefd-29a384393ed1","resolution":{"observed_at":"2026-08-14T12:59:38.332541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.317645Z","title":"Do additional features help or hurt category learning? the curse of dimensionality in human learners","venue":null,"work_id":"c9cc5b22-2d1d-4613-ab56-5718bd3d590b","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.510460Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:a2f931b8ee2e117840f2aecc5872695efbe2667606f6e69b118c023df59347f4","observation_id":"96f1f630-6c95-4acd-89c8-48e23ff9158f","resolution":{"observed_at":"2026-08-14T12:59:38.321527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.306556Z","title":"The curse of dimensionality","venue":null,"work_id":"965c6eef-b585-4fee-87ef-e9e19e077d96","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.514235Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:9ce9e3ff854e7090031308d73b97a3a27f21a9c578ea0a3221287932b28e0353","observation_id":"0ed48ce1-5d47-4d5d-b375-297d88d6e5d1","resolution":{"observed_at":"2026-08-14T12:59:38.310288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.294429Z","title":"Taxonomy-aware feature engineering for microbiome classiﬁcation","venue":null,"work_id":"f20b7e64-5b0a-4056-9c3f-61cf6e4bdece","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.518190Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:2e0db18fbe2ea66974b95ba3dd5d422029a85ee7c87d509f6237450a16caaabf","observation_id":"7fbb33df-4f72-4742-a316-7dc208ae57ae","resolution":{"observed_at":"2026-08-14T12:59:38.298665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.282684Z","title":"Stochastic shape optimization via design-space augmented dimensionality reduction and rans computations","venue":null,"work_id":"ef15822b-647e-4940-b08c-dadf777cbfff","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.522691Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:0be5667e2a06e9020f39e178dc8d5b4607936fdd865af1001e848b7c798f97f3","observation_id":"9972fb09-da3f-4fa2-b358-623dec37239b","resolution":{"observed_at":"2026-08-14T12:59:38.286861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.270489Z","title":"Big data classiﬁcation using scale-free binary particle swarm optimization","venue":null,"work_id":"9b4bf955-1b07-45ba-a31b-4ed8ddd6ff35","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.526581Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:b04e235478d9bc94dd3b0fd9241025627cdee2b1d0601f06276c1034ff518bd4","observation_id":"25683348-45e9-4b53-bccc-430df7e54ad4","resolution":{"observed_at":"2026-08-14T12:59:38.274594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.258446Z","title":"Svsa: a semi-vortex search algorithm for solving optimization problems","venue":null,"work_id":"c7ff3e4c-fda4-4981-bd11-5b004f73ef9d","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.530710Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:5322cb8910b3c9c9ff818c9910fef9626033358d69802cdc49d09a975768a5a6","observation_id":"ee6acf5d-13c0-4c5b-9542-736d24cf1dc9","resolution":{"observed_at":"2026-08-14T12:59:38.262391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.246656Z","title":"An accelerated introduction to memetic algorithms","venue":null,"work_id":"266a3583-a674-49a9-826c-1fc095fe4bb3","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.534562Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:567e060431a490aede371e93d62b9562152e44e83dc8c6447147f7b869213bcb","observation_id":"eb1d7e11-4002-4dcc-9f03-35d5cdeb4f4d","resolution":{"observed_at":"2026-08-14T12:59:38.250785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.538553Z","title":"An eﬃcient feature generation approach based on deep learning and feature selection techniques for traﬃc classiﬁcation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.538553Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:c5142673c9cd15a083e75d1eda4a2feb718638500c8e4450dc03e49ace841aab","observation_id":"dfa26180-acd6-44e5-a42c-ca8a704fe7b6","resolution":{"observed_at":"2026-08-14T12:59:37.538553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.225091Z","title":"Feature engineering for pre- dictive modeling using reinforcement learning","venue":null,"work_id":"087f3b63-f9b6-4d24-91b7-1dcc4d3fdc8b","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.542388Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:8c8d51f4514e58f820e4fe962d86a93257f5f58f2b39f68f515b440e5bc5204a","observation_id":"7fc6ff0d-ec7b-4e60-a4fb-23be86884a3a","resolution":{"observed_at":"2026-08-14T12:59:38.230237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.212892Z","title":"Network representation learning: A survey","venue":null,"work_id":"3b818d1b-a657-4d7c-9b04-a1f1698ab2b0","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.545999Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:b2471fa8bc17aaae23cbaa50a2856df31c9bd849b7fa56b952cae90766afc746","observation_id":"7df023ef-ebd6-4734-8ed4-f765b2f445a7","resolution":{"observed_at":"2026-08-14T12:59:38.217190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.199945Z","title":"Novel wrapper-based feature selection for eﬃcient clinical decision support system","venue":null,"work_id":"a32921d4-9632-47cb-a092-38aeba6692db","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.549952Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:fc2a3e4b7306e44d3e40e41fa4912ae63dd4ed2513dcee4e387261dcecb8eeda","observation_id":"c19f1752-fdd5-4295-9d0d-e34ade85df75","resolution":{"observed_at":"2026-08-14T12:59:38.204897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.553977Z","title":"Pareto front feature selection based on artiﬁcial bee colony optimization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.553977Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:439f243586a3e8bbde39c2d0d11780e2ec14354874f9f8e51c872b6b685da5eb","observation_id":"62f88c23-1996-45aa-9a65-d899e47289d6","resolution":{"observed_at":"2026-08-14T12:59:37.553977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.178287Z","title":"A hybrid ge- netic algorithm with wrapper-embedded approaches for feature selection","venue":null,"work_id":"93a4bc0e-717d-4d17-a031-033eabc5c401","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.557399Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:d39f3bfc4d9358e64b4bb206e7a64fd882016d330c8be51b08dfaa56cde90c99","observation_id":"df015431-2469-4c7f-8d12-31ff35322066","resolution":{"observed_at":"2026-08-14T12:59:38.183480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.164260Z","title":"Particle swarm optimization based feature selection with novel ﬁtness function for image steganalysis","venue":null,"work_id":"005bb60a-3e39-4e74-8965-aeeff284c9cc","year":2016},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.560878Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:7c8acb07af12ee38b825594592602dace00e5f90d37ca345fc1f00f209f0ed12","observation_id":"94dfef07-0186-4b31-863d-07a6fe8cd8c2","resolution":{"observed_at":"2026-08-14T12:59:38.169333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.152719Z","title":"A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization","venue":null,"work_id":"e8980480-cb0b-4f99-8b05-d3cb394116db","year":2016},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.564564Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:7b9a739362489dffa53b0674fb15821241ce008f2e6b4673b201e067b1f6407b","observation_id":"ba0aaaab-5188-46c0-a798-646311d23f9e","resolution":{"observed_at":"2026-08-14T12:59:38.156744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.141813Z","title":"Simultaneous allo- cation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks","venue":null,"work_id":"38aaa641-1a4f-4152-9840-f7334c4788b2","year":2017},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.568469Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:f3d1d0e15b642e6fa653950b280ed96d75e038671329654c340af49f5845242b","observation_id":"72bd01e6-dcfc-403d-a389-1d8afe283441","resolution":{"observed_at":"2026-08-14T12:59:38.145479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.129883Z","title":"Genetic algorithms in resource optimization of con- struction project","venue":null,"work_id":"682cbb04-0ac5-4229-bc81-cc924c90f471","year":2001},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.572617Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:2ae9eb0d26cac1968aa853b01746171adb5d9c6550771ace12a45c22b9254050","observation_id":"746fb719-0c0e-4206-9a9a-038ce98edf03","resolution":{"observed_at":"2026-08-14T12:59:38.133555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.576236Z","title":"Allocation of electric vehicles’ parking lots in distribution network","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.576236Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:c71ce640675bafe9f4b8f6e5f1fc413fb7a98a492037c3e1d3fcc2b9c1b0dbeb","observation_id":"8e66e96e-f2fa-42f6-8525-d1b6648ab5df","resolution":{"observed_at":"2026-08-14T12:59:37.576236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.579875Z","title":"Genetic programming: on the programming of computers by means of natural selection, volume 1","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.579875Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:d9ef3ab836661ba39bf3f53a25768d73ae3e14844bfe124da6794634c52be2ac","observation_id":"de1c7088-e83f-4bfb-aef5-cc3ca3b597ad","resolution":{"observed_at":"2026-08-14T12:59:37.579875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.099884Z","title":"Knowledge discovery in multiobjective optimization problems in engineering via genetic programming","venue":null,"work_id":"2677b9aa-249a-4bcc-9a52-1ffa9bb44fbb","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.583964Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:563895a22516e7a0ab7665133077ce921faecf2ee965caa91577acaeb6b17b83","observation_id":"083b1d81-2a77-46b2-bf24-8dde7f01ace6","resolution":{"observed_at":"2026-08-14T12:59:38.104161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.087880Z","title":"Artiﬁcial intelligence through a simulation of evolution","venue":null,"work_id":"0be0bb37-f639-44f6-a4f9-63dd78f5c07c","year":1965},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.588015Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:aa3317a5049170eabbbedd9d3edb220dc52794ac19aca6055de668c8eebb0294","observation_id":"6d6d4c1f-6484-493f-8337-b0f65970c05c","resolution":{"observed_at":"2026-08-14T12:59:38.091871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.075762Z","title":"A comprehen- sive survey: artiﬁcial bee colony (abc) algorithm and applications","venue":null,"work_id":"f2914460-1522-4d90-b05c-c43df2671144","year":2014},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.591424Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:8950b3932ac65b56ea1a93e23228743e626ddaabcbb3bba13585b02b0ed3d9ba","observation_id":"d11a10f1-a99a-4994-bf56-634d04b4445c","resolution":{"observed_at":"2026-08-14T12:59:38.080336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.063957Z","title":"An idea based on honey bee swarm for numerical optimization","venue":null,"work_id":"a9bf1898-7c22-4821-a69f-12ba012f19d1","year":2005},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.594775Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:72fc73a46fdb65fa8972939f765de56cb5829dde136277b6b7aa34a1ce556b02","observation_id":"2992a394-6b54-42c9-a93d-1ef5a695610a","resolution":{"observed_at":"2026-08-14T12:59:38.067791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.051081Z","title":"A novel history-driven artiﬁcial bee colony algorithm for data clustering","venue":null,"work_id":"6b13f917-1d2e-41ff-ae44-382275063b30","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.598931Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:1078b1840ee4b69460c0efc708b0ed6be996f03551031b700d76c7f2f7dce9d0","observation_id":"9408ffcc-8f4e-47c3-a16c-a12d396b23d4","resolution":{"observed_at":"2026-08-14T12:59:38.055803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.602043Z","title":"An improved global best guided artiﬁcial bee colony algorithm for continuous optimization problems","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.602043Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:97be7f25d837a17409b44033ace280ac5f0eb5c5818b982b3a05d885b2311496","observation_id":"c4c30ab9-86e4-4560-9e09-a69dcb0709fb","resolution":{"observed_at":"2026-08-14T12:59:37.602043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.032453Z","title":"A self-adaptive artiﬁcial bee colony algorithm based on global best for global optimization","venue":null,"work_id":"6673b203-6aa0-4856-b7e6-3cf4f35a20c7","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.605332Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:3c730399dd3379acb4cd1b9bb4b2e830a0af6180619b53f2d63dda812627e8b3","observation_id":"00100965-e4af-48bd-8f10-a7b364157350","resolution":{"observed_at":"2026-08-14T12:59:38.036384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.020524Z","title":"Modiﬁed multiple search cooperative foraging strategy for improved artiﬁcial bee colony optimization with robustness analysis","venue":null,"work_id":"d556ab69-2c3a-4039-87ce-6f948c8bc1d7","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.611106Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:e253e7f4d7e6bfb9a92178f398aeb0df9f94c847586f6f5a718ae9a6e79d5e16","observation_id":"06520afa-2f7b-4332-92cb-b6cae4be2669","resolution":{"observed_at":"2026-08-14T12:59:38.024984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:38.006277Z","title":"An improved optimization method based on krill herd and artiﬁcial bee colony with information exchange","venue":null,"work_id":"2b544cf5-1cc4-4f9d-afd2-1fb8b2958ee1","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.615155Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:6920dbd5a1b0b57e5907eabd056851ba59b2af72dfd7a172f0ac527dddf748af","observation_id":"5407cd07-5e56-4076-b63d-6ae75f97d573","resolution":{"observed_at":"2026-08-14T12:59:38.010618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.992909Z","title":"Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection","venue":null,"work_id":"d4eed0c7-9a7b-4f42-ae89-aa94d5dc41fe","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.619954Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:75a737a6c3f275038e789eb61e61c93d27de52cbb1009d9c01856809c4853821","observation_id":"e53b0c38-266f-4a5d-aae9-0b94b8cada91","resolution":{"observed_at":"2026-08-14T12:59:37.997933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.983290Z","title":"Eberhart","venue":null,"work_id":"9747a33b-541f-4b64-a10f-c3e97389af4a","year":1942},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.623732Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:ad3c20ba76a394bb24327bb546c910e6806b209642dcdbd73140c6714ae1af3a","observation_id":"ccd1e1e1-290a-4714-b534-ac4a5e2c398c","resolution":{"observed_at":"2026-08-14T12:59:37.986004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.974058Z","title":"Particle swarm optimization","venue":null,"work_id":"876784a1-42ba-4677-82ff-78922c62276f","year":2010},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.627798Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:f0225226855eae335277a836eea03ea626848617235e1d64aaf84fb508d1082f","observation_id":"20cc48f9-8ec4-4319-8ab1-c98fc4c9949d","resolution":{"observed_at":"2026-08-14T12:59:37.977053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.964010Z","title":"A particle swarm optimization algorithm with crossover operator","venue":null,"work_id":"edb5f62e-d824-4f24-a6d9-84e31d5b4b85","year":2007},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.631445Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:c4c17a1eaa05d81060cf918e77c294802755e3244ca6d1d26d893b9688b52349","observation_id":"32749904-f4a8-4e73-8b60-e5202d8f6b5f","resolution":{"observed_at":"2026-08-14T12:59:37.967715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.952301Z","title":"Binary pso with mutation operator for feature selection using decision tree applied to spam detection","venue":null,"work_id":"10255548-3526-4cdf-9ed6-9f2aceac440a","year":2014},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.635029Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:cff7a30340e7a324475777d986ed91429ac0e3b442f253d4b580b11ee6ded224","observation_id":"a3ce0274-7c9d-4e6d-883b-a8d19fefaaf8","resolution":{"observed_at":"2026-08-14T12:59:37.956718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.941808Z","title":"Particle swarm optimization with probabilistic inertia weight","venue":null,"work_id":"506ab96b-63c2-4da5-a45e-93d844affa4d","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.638779Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:64d114a5f382e369767c6e9b89674eff7abe85ae95b3998951e21cd3e48cba38","observation_id":"4c15accc-890b-434a-92dc-6ac4e97edcdc","resolution":{"observed_at":"2026-08-14T12:59:37.945547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.930900Z","title":"Optimal design of power-system stabilizers using particle swarm optimization","venue":null,"work_id":"78cbaac1-0903-4a45-a05b-8d6357a71eca","year":2002},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.642363Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:6b16ea18950c828bff888f3fbd97f2eb2bae1f7012de0f503121e5f5943a81fe","observation_id":"c9cab9fb-940f-48bf-b4e9-d4a5a394a7c6","resolution":{"observed_at":"2026-08-14T12:59:37.935228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.919229Z","title":"Practical distri- bution state estimation using hybrid particle swarm optimization","venue":null,"work_id":"033b4161-c63d-4ac9-8947-797960eaedfb","year":2001},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.646575Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:0bc2ca50db1ca2dc4746c4618a8edb8f12e91063e78737387783a54910814240","observation_id":"70f7d4da-2254-432d-9d86-63b9d44f7ba6","resolution":{"observed_at":"2026-08-14T12:59:37.923507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.904938Z","title":"A particle swarm optimization for reactive power and voltage control consider- ing voltage security assessment","venue":null,"work_id":"4b43d82f-1d3f-45dd-9024-8948766d04d9","year":2000},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.650174Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:401fabdac5b4ad84f99276409e4f111974108924c9b46ced264bf6a426fdbef7","observation_id":"7d2c66ab-a9ac-4b40-acee-21b9992586ea","resolution":{"observed_at":"2026-08-14T12:59:37.909434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.892380Z","title":"Ant colony optimization: a new meta-heuristic","venue":null,"work_id":"c08df535-af62-4285-ae37-db1c5e740a83","year":1999},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.653916Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:9517d750a1acc7e512e1501099adba272f926652273aa8c17435c6f3f9173bfe","observation_id":"9c01bdc5-9f54-4e6d-86ae-1a59c7c025e2","resolution":{"observed_at":"2026-08-14T12:59:37.896745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.881032Z","title":"Ant colony optimization for continuous domains","venue":null,"work_id":"5d89793a-171a-4914-ae63-2c596c6feda9","year":2008},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.657694Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:7da9a1ab7c50baaedb242c9c6a59c37e22c8ed714332a17ff02fe8b084de7a82","observation_id":"472f271c-3b40-40bf-973a-3bb7938fe052","resolution":{"observed_at":"2026-08-14T12:59:37.884738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.870759Z","title":"A new hybrid ant colony op- timization algorithm for feature selection","venue":null,"work_id":"11d65edb-31f7-463e-b737-e933493137f5","year":2012},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.661180Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:dda806f16e9d1f0430d62672834cd166374e923d715a08842934b14e35acdb62","observation_id":"408fe32b-6737-4a15-a1f9-10fff118f263","resolution":{"observed_at":"2026-08-14T12:59:37.874107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.860157Z","title":"Grey wolf optimizer","venue":null,"work_id":"c4c2beb0-d64e-486b-a204-1e9fe3168421","year":2014},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.664866Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:8588151f62ad0b69a49deb75a4b1f47a2c91b33ceafd610234384309bbff8436","observation_id":"9ca84ada-18d8-43a3-b7a8-aa7690cf66bd","resolution":{"observed_at":"2026-08-14T12:59:37.863699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.848340Z","title":"A new bio-inspired algorithm: chicken swarm optimization","venue":null,"work_id":"dec01e8b-572c-475b-bb73-be28773d482b","year":null},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.669081Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:cb59f1539fa753beaf138461b41c4e197ba564d5000f75c0b2e249cc0a913d71","observation_id":"4b142880-bef2-4215-ad6e-e8c5b7b31f71","resolution":{"observed_at":"2026-08-14T12:59:37.852837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.836506Z","title":"Optimizing directional reader antennas deployment in uhf rﬁd localization system by using a mpcso algorithm","venue":null,"work_id":"2ce554a0-19b8-4ffe-9437-6172e5d655a5","year":2018},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.673060Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:4c4f23a1cc268cff46e4a72a3746267fe4f3bb09e65da3efcda0903d12db1d18","observation_id":"120b6ced-2e72-4916-af4c-66c687a8e526","resolution":{"observed_at":"2026-08-14T12:59:37.840723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.823646Z","title":"An adaptive ap- proach for community detection based on chicken swarm optimization algorithm","venue":null,"work_id":"b2e3441f-db75-48d4-9163-142556553293","year":2016},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.676927Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:155e96652a39fe6636d43278fd6bf44f27c1b38dfb3e9a8c9dc5df2a11767ef3","observation_id":"9fb429aa-e6fc-4e4f-b7fc-d7b768f4529d","resolution":{"observed_at":"2026-08-14T12:59:37.827661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.811917Z","title":"Dempster-shafer based probabilistic fuzzy logic sys- tem for wind speed prediction","venue":null,"work_id":"f99dec8d-b972-4b9e-85d4-5f7064767d9f","year":2017},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.680731Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:e4e446e8171cda12d244ad12ac3f875e53eeecfee9b38436c2332a8a40a56993","observation_id":"09c6e609-080a-4a99-a4f8-d879204a1edd","resolution":{"observed_at":"2026-08-14T12:59:37.815884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.684210Z","title":"An optimizing method based on autonomous animats: ﬁsh-swarm algorithm","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.684210Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:66228c01ca32ff0be5bf8d992d3a4113e51d58710b260df6e087a34b7ca4c0a1","observation_id":"f40cdc64-309c-4676-a31f-d5a4a436d0c0","resolution":{"observed_at":"2026-08-14T12:59:37.684210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.792014Z","title":"Neighborhood rough set reduction with ﬁsh swarm algorithm","venue":null,"work_id":"79cf45ea-3e4f-4e4c-8cbe-0261a0269c19","year":2017},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.687812Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:e9f23172d37eb9481c4370b38584e79455952dad910d349e406ddd1238425279","observation_id":"ecb10668-c185-4a8b-9955-912535419375","resolution":{"observed_at":"2026-08-14T12:59:37.796001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.779362Z","title":"Artiﬁcial ﬁsh swarm- inspired whale optimization algorithm for solving multimodal benchmark functions","venue":null,"work_id":"f51c2f17-43c8-4a06-a206-a2561454f4b4","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.691466Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:be7501420274abb2c62b12378d4f1c1d1f8d9820cd57d6812a50ee76260bc9e6","observation_id":"b506b153-81b8-441c-a290-7b439d57ed53","resolution":{"observed_at":"2026-08-14T12:59:37.783956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:59:37.768193Z","title":"Applications of nature-inspired algorithms for dimension Reduction: Enabling eﬃcient data analytics","venue":null,"work_id":"721e7fb6-9997-4994-a424-f0ccbaf5a706","year":2019},"citing_paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-14T12:59:37.695086Z"},"links":{"citing_paper":"/paper/1908.08006"},"observation_digest":"sha256:49cb6d0b9491eb7f3bbe42fc9781fb438ab65dc51870fd8b722cea483f366da4","observation_id":"537cc78b-f226-4963-9537-1bbac8ce0b2f","resolution":{"observed_at":"2026-08-14T12:59:37.771738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.08006","last_updated":"2019-08-16T17:16:16Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-14T12:54:39.484138Z","submitted_at":"2019-08-16T17:16:16Z","title":"Evolutionary Computation, Optimization and Learning Algorithms for Data Science"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":2,"verified_fuzzy":55},"total_outbound_references":67},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 2 inbound Pith citation observations for arXiv:1908.08006."}