{"as_of":"2026-08-15T05:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0cc79b34e77e7262e1c106ec57f335150bbe6276af67189a1074bba57734aa23","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:20:14.965115Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.11016/citation-record","integrity":"/paper/2509.11016/integrity","json":"/paper/2509.11016/citation-record.json","paper":"/paper/2509.11016"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:20:13.914239Z","title":"Engineering design: a sys- tematic approach,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:13.914239Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:04decf8115097760c212c96997b2ac7644744dfe85186d40bfa22c7295a1e47e","observation_id":"4261e13b-6475-4f78-bb36-18820372d467","resolution":{"observed_at":"2026-08-04T17:20:13.914239Z","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":"2026-08-04T17:20:14.002440Z","title":"A knowledge- guided bi-population evolutionary algorithm for energy-efficient scheduling of distributed flexible job shop problem,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.002440Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:19446f7c8cb8a6e89497ce5e9f5120e59dd0f032a22ba7486d752385dd4879f0","observation_id":"7fb34a87-3282-459b-ad54-907eef20bb22","resolution":{"observed_at":"2026-08-04T17:20:14.002440Z","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":"2026-08-04T17:20:14.079442Z","title":"Path planning techniques for mobile robots: Review and prospect,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.079442Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:43469d33ad69e1ec4802c174af92e26e660e51e344eb713357e0c5b3075346ba","observation_id":"378b913e-3492-440f-a907-80818e881080","resolution":{"observed_at":"2026-08-04T17:20:14.079442Z","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":"2026-08-04T17:20:14.125617Z","title":"Review of ship arrangement design using optimization methods,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.125617Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:5acc71ba27c8740f426a69f360f7aa74b2b5fb77e935f378694e90d8a7d3b700","observation_id":"0c0c4542-9e9e-4432-8183-8d68cdbdbad8","resolution":{"observed_at":"2026-08-04T17:20:14.125617Z","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":"2026-08-04T17:20:14.147328Z","title":"A brief review of portfolio optimization techniques,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.147328Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:9977deff21946deef9fbaf2e6ede79fd3dc1588e4faf6d2d2d2827a287edb4a6","observation_id":"8f4d44f3-57f3-4858-8130-72eb653e86e0","resolution":{"observed_at":"2026-08-04T17:20:14.147328Z","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":"2026-08-04T17:20:14.178815Z","title":"An adaptive scheduling system with genetic algorithms for arranging employee training programs,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.178815Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:74b7465b1b82e8f08409dc3d98c1d9ac2879ed141cf14ee4200aa4c85280d368","observation_id":"5df58aa4-9d0d-4f65-8187-9c81e28f6873","resolution":{"observed_at":"2026-08-04T17:20:14.178815Z","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":"2026-08-04T17:20:14.209978Z","title":"Constraint-handling techniques used with evolutionary algorithms,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.209978Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:13978db36b76a702ecd528dca670ade308b23b9794980faef3484c677fbe347e","observation_id":"3d535312-f038-43e8-94db-7ac03a5db71f","resolution":{"observed_at":"2026-08-04T17:20:14.209978Z","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":"2026-08-04T17:20:14.323330Z","title":"A review on constraint handling techniques for population- based algorithms: from single-objective to multi-objective opti- mization,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.323330Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:ec8c0c404216d2dcebb6d639b1630ae91748bbd97af14ab9606665c9b99d10b5","observation_id":"3f1935d4-498d-44a1-8cef-ad7efabdb029","resolution":{"observed_at":"2026-08-04T17:20:14.323330Z","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":"2026-08-04T17:20:14.385113Z","title":"A survey on evolutionary constrained multiobjective optimization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.385113Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:60a6c5c3c689495560955acbebfb2f98982f61f675d1ba0c2bcd8317ee78ca4b","observation_id":"6e25bf67-3704-429b-8cad-93ddb61d94ae","resolution":{"observed_at":"2026-08-04T17:20:14.385113Z","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":"2026-08-04T17:20:14.527488Z","title":"Two-archive evolutionary algorithm for constrained multiobjective optimization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.527488Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:fe025a9c64fc1566b5215fd3e8fa1d8f59ebe21c86f4c7c5da96b2702fe769da","observation_id":"62045cd5-4253-45f1-801d-dcfc377b89aa","resolution":{"observed_at":"2026-08-04T17:20:14.527488Z","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":"2026-08-04T17:20:14.700826Z","title":"A novel differential evolution algorithm for solving constrained engineering optimization problems,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.700826Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:620cf4e08a7047532d76f297b40e60ab89ee0f63a06a540a2ba18a97665112f7","observation_id":"c9746ffa-10dd-481c-8df5-72bb25cfcb5a","resolution":{"observed_at":"2026-08-04T17:20:14.700826Z","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":"2026-08-04T17:20:14.814013Z","title":"A constrained multiobjective evolutionary algorithm with detect-and-escape strategy,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.814013Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:80ed3793968207f6f52f089e3155eaec5d61d7b23b2aa8513adf05ef1f83e032","observation_id":"a784d620-5066-4db1-bc36-91ce76c4ca9b","resolution":{"observed_at":"2026-08-04T17:20:14.814013Z","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":"2026-08-04T17:20:14.823632Z","title":"A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combina- torial optimization of trauma systems,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.823632Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:03bfce926a1f5d4cc0dfd9fd4d2dff79df92f58a266daa05cd6ce2eaca65e74f","observation_id":"7186db47-f896-40ef-848a-28e6f85c19bc","resolution":{"observed_at":"2026-08-04T17:20:14.823632Z","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":"2026-08-04T17:20:14.827753Z","title":"A dual-population-based evolutionary algorithm for constrained multiobjective optimization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.827753Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:4f9c646def3aa6e21a142fd397b064c22141069a573b5c986f6bb6e48d98f623","observation_id":"b06eb004-8d2b-47d0-bda4-d5e5edbb9a1b","resolution":{"observed_at":"2026-08-04T17:20:14.827753Z","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":"2026-08-04T17:20:14.831685Z","title":"Constrained multi- objective optimization with deep reinforcement learning assisted operator selection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.831685Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:8095492f553d1a4468d59e1d3eebd17a3aab5cde3cc7123a25ead0d68745cdc7","observation_id":"af3b10ed-d34d-400b-9284-304bc985e405","resolution":{"observed_at":"2026-08-04T17:20:14.831685Z","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":"2026-08-04T17:20:14.836009Z","title":"Ensemble strate- gies for population-based optimization algorithms–a survey,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.836009Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:8f9d56460e36b8c6285b5bfd3599c80da2b15492d9d3e21fbbeae27e71a0854f","observation_id":"7553421a-dd87-464c-b994-67beba1d7011","resolution":{"observed_at":"2026-08-04T17:20:14.836009Z","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":"2026-08-04T17:20:14.839716Z","title":"Adaboost-inspired multi-operator ensemble strategy for multi-objective evolution- ary algorithms,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.839716Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:c5062734c80d43d443445a7f76f7988085168cdb62baa0e47bc3ec0a44830d49","observation_id":"839d1bdd-6b5e-4d03-8296-e8ddc1059ee4","resolution":{"observed_at":"2026-08-04T17:20:14.839716Z","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":"2026-08-04T17:20:14.844145Z","title":"Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.844145Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:d03e9c1614e18fa6aad3271c6550b89b3a79b520f65d4464aa7a255183d5b665","observation_id":"19298425-e6c1-41ac-b5b1-322fe7cf22fd","resolution":{"observed_at":"2026-08-04T17:20:14.844145Z","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":"2026-08-04T17:20:14.848439Z","title":"A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classifica- tion,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.848439Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:5e68300072020aef1079742e653565fabf8aa7165e83a13e10b557810b81bb88","observation_id":"520289ff-5fa4-4143-bb57-63e10aadaaaa","resolution":{"observed_at":"2026-08-04T17:20:14.848439Z","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":"2026-08-04T17:20:14.852570Z","title":"An adap- tive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.852570Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:9ead768f7d708283a170a367336fac3ae361f2c0eb9e816d3eb8e90a2cfd1e8f","observation_id":"a4287e86-aaaa-49a9-b6c2-fbe2d70c8e0f","resolution":{"observed_at":"2026-08-04T17:20:14.852570Z","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":"2026-08-04T17:20:14.856600Z","title":"Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.856600Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:7a3765388f55b9276fa1c8ddfeb53ae0fb1d2ddc9f3e1b091f91da285d70290a","observation_id":"89e6f516-66d9-4f33-993f-c6c42cdb11bd","resolution":{"observed_at":"2026-08-04T17:20:14.856600Z","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":"2026-08-04T17:20:14.860957Z","title":"Deep reinforcement learning-guided coevolutionary algorithm for constrained mul- tiobjective optimization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.860957Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:96f603debd99ca26262175c77f43f4ca941cfc057db144444bafd371a7db9d19","observation_id":"43f84593-3c10-4704-9f05-f83f4d07934e","resolution":{"observed_at":"2026-08-04T17:20:14.860957Z","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":"2026-08-04T17:20:14.864868Z","title":"Deep reinforcement learning: A brief survey,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.864868Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:8d7848194c56959e91593d85bde3ca3842c71a635f2c891cc12fbb15580e400f","observation_id":"58770034-d3eb-48e4-8010-be8024ba9bb4","resolution":{"observed_at":"2026-08-04T17:20:14.864868Z","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":"2026-08-04T17:20:14.869104Z","title":"Deep reinforcement learning for au- tonomous driving: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.869104Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:814c098094bc765b108cd2300498b5b78b82ca650569de225ba80c7b2df3c220","observation_id":"1c2faf47-2f9f-4f3d-993c-cb8c7632682f","resolution":{"observed_at":"2026-08-04T17:20:14.869104Z","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":"2026-08-04T17:20:14.873141Z","title":"Bi-phase episodic memory-guided deep reinforcement learning for robot skills,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.873141Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:bfbd92c32f84af95eec415b2d6b85954f3ebe77e0794447559c6f99a1f3cb74a","observation_id":"90632e6d-f522-4937-bf70-df81f8415b05","resolution":{"observed_at":"2026-08-04T17:20:14.873141Z","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":"2026-08-04T17:20:14.877026Z","title":"Applications of deep reinforcement learning in communications and networking: A survey,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.877026Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:bf63da57d493963174d166b92ca8a5183acaab2e753d22a50a15522e93076372","observation_id":"23fec9bd-7c14-42d7-81a9-630f65aa1eee","resolution":{"observed_at":"2026-08-04T17:20:14.877026Z","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":"2026-08-04T17:20:14.881097Z","title":"Reconfigurable intelligent surfaces for wire- less communications: Principles, challenges, and opportunities,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.881097Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:5e94462b121d20ada1e92b82244ee56f2f2730b463159f0b203eb147d7ee67c5","observation_id":"693798ee-fb3e-4d66-b687-74b10dd4494f","resolution":{"observed_at":"2026-08-04T17:20:14.881097Z","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":"2026-08-04T17:20:14.885049Z","title":"A self-adaptive evolutionary multi-task based con- strained multi-objective evolutionary algorithm,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.885049Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:25fd0babefc258402cb375f5cd9f536a8b1fa8f20f195c18a92e1b272fa1420e","observation_id":"f9cb1181-6bcd-4005-b702-ca5c79da930c","resolution":{"observed_at":"2026-08-04T17:20:14.885049Z","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":"2026-08-04T17:20:14.889449Z","title":"A constrained multiobjective evolutionary algorithm based on adaptive con- straint regulation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.889449Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:f5ebc89a241dfefe03be9658f951fca1eeae578b83d967f42386c0556f5d8590","observation_id":"6998b49e-65c7-4341-86d2-4b746ca39ea5","resolution":{"observed_at":"2026-08-04T17:20:14.889449Z","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":"2026-08-04T17:20:14.893540Z","title":"A dual- population evolutionary algorithm based on adaptive constraint strength for constrained multi-objective optimization,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.893540Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:a1fe3142450d86ef58e45842d7b9c3134bd3d4d888ee00f5338efda7b72f6a2f","observation_id":"c759ac36-9e9c-41ec-a792-03f5ed99a554","resolution":{"observed_at":"2026-08-04T17:20:14.893540Z","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":"2026-08-04T17:20:14.897537Z","title":"Aschea: new results using adaptive segregational constraint handling,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.897537Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:ba5beb5cdfa669e64809bf33024ca41854185acb369f1703c732291a407ab874","observation_id":"71a72cd9-6a27-465b-8a80-6ccab88d2326","resolution":{"observed_at":"2026-08-04T17:20:14.897537Z","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":"2026-08-04T17:20:14.901601Z","title":"Dynamic neighborhood hybrid par- ticle swarm optimization for constrained optimization,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.901601Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:19e915dc6fc008ec733e92e9330545348c934f9ddc0a3d8af2600423387199bd","observation_id":"1e128947-6bf1-43e5-b559-d6e71a8de735","resolution":{"observed_at":"2026-08-04T17:20:14.901601Z","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":"2026-08-04T17:20:14.905418Z","title":"Efficient constrained optimization by the ε constrained adaptive differential evolution,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.905418Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:fc18630a7ccb8c8031db9b1f9c1c140d8614abdfaeb2c60d699bcfb0562a31ae","observation_id":"a8ad31d7-193d-46a5-b8a0-457eff7393ea","resolution":{"observed_at":"2026-08-04T17:20:14.905418Z","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":"2026-08-04T17:20:14.909240Z","title":"Deep reinforcement learning for au- tonomous driving: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.909240Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:151f9faf539616de50a9ff452a34ac554e7f80a932675f56d0623a0aa487ccb6","observation_id":"b4f3803a-1515-4ae6-b1a3-846602e257c4","resolution":{"observed_at":"2026-08-04T17:20:14.909240Z","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":"2026-08-04T17:20:14.913923Z","title":"Deep reinforcement learning control for radar detection and tracking in congested spectral environ- ments,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.913923Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:e6145ad0a604d0970544564e731d4f308dfb55afc7784981d0d772a005b60946","observation_id":"85ac2ae3-b866-4cb1-9171-3692b9d2ac6c","resolution":{"observed_at":"2026-08-04T17:20:14.913923Z","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":"2026-08-04T17:20:14.917941Z","title":"Deep reinforcement learning based mobile robot navigation: A review,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.917941Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:cc1fb0f44a53331f8aafa79d8761e1d458bdd5a3e537f97fca73e0074f17847f","observation_id":"b335b1de-9dfb-4b18-96be-d6948e0de6f3","resolution":{"observed_at":"2026-08-04T17:20:14.917941Z","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":"2026-08-04T17:20:14.921879Z","title":"Adaptive constraint handling technique selection for constrained multi-objective op- timization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.921879Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:77a911b0a3dfb91cf932f03ce071f59be90cceff84f4ba5dae3b5ad07d3cea33","observation_id":"bf78f92e-7ccf-4db8-9bbd-3239bcb5d07d","resolution":{"observed_at":"2026-08-04T17:20:14.921879Z","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":"2026-08-04T17:20:14.926109Z","title":"Principled design of translation, scale, and rotation invariant variation operators for metaheuristics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.926109Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:003d3a522572dbbc58ab8fddf53f139650e1b38243f1d7b3d91496ccc8e99d93","observation_id":"12951e19-374c-415c-a84a-cab61c36ee3f","resolution":{"observed_at":"2026-08-04T17:20:14.926109Z","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":"2026-08-04T17:20:14.929942Z","title":"Problem definitions and evaluation criteria for the cec 2010 competition on constrained real-parameter optimization,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.929942Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:4d17f581471f872776ee521f0f783063a81cf56b26b61a4337e2a48c15f35246","observation_id":"3b9c1ef8-ce25-4fee-9261-6fd5cf6fd46e","resolution":{"observed_at":"2026-08-04T17:20:14.929942Z","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":"2026-08-04T17:20:14.934162Z","title":"Problem defini- tions and evaluation criteria for the cec 2017 competition on constrained real-parameter optimization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.934162Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:f2fdcc2d78fe28c9039da35800e22c8efadaafdf5c6b1d7be656a2bf0a407459","observation_id":"8b218ac2-c1a7-44e6-ac08-7fd03fac357d","resolution":{"observed_at":"2026-08-04T17:20:14.934162Z","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":"2026-08-04T17:20:14.937788Z","title":"Stochastic ranking for constrained evolutionary optimization,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.937788Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:8649de70409166589563d7cab2f35bc3b0c626e9560db6237317c77a440c6869","observation_id":"03c4bfd0-0864-460a-98c3-8cc50ec506c1","resolution":{"observed_at":"2026-08-04T17:20:14.937788Z","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":"2026-08-04T17:20:14.941338Z","title":"Building scalable test problems for benchmark- ing constrained optimizers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.941338Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:19d91b882bb50d492a046673cf811a23af0891c308e8e3d2ff60c75fbbfb04d1","observation_id":"37e265eb-f97b-481b-981d-a7536fcbbf27","resolution":{"observed_at":"2026-08-04T17:20:14.941338Z","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":"2026-08-04T17:20:14.945083Z","title":"Differential evolution–a simple and effi- cient heuristic for global optimization over continuous spaces,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.945083Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:4dac4e3e4556f3844ced4fee7258ad7c6fce253b0a579a3ee5058cfea1d3670d","observation_id":"db7e4127-3d91-45ed-84b6-c9f2cf331799","resolution":{"observed_at":"2026-08-04T17:20:14.945083Z","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":"2026-08-04T17:20:14.948764Z","title":"Combining multiobjective optimization with differential evolution to solve constrained optimization problems,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.948764Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:3b1b453eec28ecd201b4a34d0b6e0f03a5c1fca6109cd490d9a6452bdbdffe2d","observation_id":"02a7306a-2285-48b5-9f27-161901987872","resolution":{"observed_at":"2026-08-04T17:20:14.948764Z","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":"2026-08-04T17:20:14.952736Z","title":"Adaptive differential evolution with multi-population-based mutation operators for constrained optimization,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.952736Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:e84c5af0f28bf02bcfadc07d887ae476f211bb71606c9f78f8d5df653f0c58a2","observation_id":"cefc0ec1-39c1-4ccf-aa14-ee31b8d4d177","resolution":{"observed_at":"2026-08-04T17:20:14.952736Z","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":"2026-08-04T17:20:14.957296Z","title":"An enhanced adaptive differential evolution approach for constrained opti- mization problems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.957296Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:7c4a76f1082fc99a1fee1fa7f6d789c654502fe3d5e046669b318df02d116cbe","observation_id":"d9705c3d-bf78-4ce0-bf2b-f71e3b2cec4c","resolution":{"observed_at":"2026-08-04T17:20:14.957296Z","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":"2026-08-04T17:20:14.961215Z","title":"Composite dif- ferential evolution for constrained evolutionary optimization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.961215Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:e31216e5016eb1d0d7cf1a653e6105df17ad5ed0641d1939b754066c1d403e60","observation_id":"7dcebc35-fc4f-4ce0-a56a-6e9e8fb0aed4","resolution":{"observed_at":"2026-08-04T17:20:14.961215Z","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":"2026-08-04T17:20:14.965115Z","title":"A multiobjective optimization-based evolutionary algorithm for constrained optimization,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T17:20:14.965115Z"},"links":{"citing_paper":"/paper/2509.11016"},"observation_digest":"sha256:da42158f07c0f4daff284705732a32bae864943a8e0fc2a25c415ac1d68c7cf4","observation_id":"1b568444-5bff-47ff-b72b-6e40fc6d9cc6","resolution":{"observed_at":"2026-08-04T17:20:14.965115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.11016","last_updated":"2025-09-14T00:14:40Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-04T17:20:13.000503Z","submitted_at":"2025-09-14T00:14:40Z","title":"Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":48,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":48},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2509.11016."}