Paper Citation Record · LEDGER

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization

As of 10 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 3 inbound Pith citation observations for arXiv:2501.19113.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.19113 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:22:24.485094Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:23:09.560026Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy17
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a2327241-988b-4bfb-a778-a6667ce717d2 · outbound

This paper cites in Kohn-Sham equations [21].

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization in Kohn-Sham equations [21]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.760437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 65c04449-17c3-4fb5-b5de-6ebb48423305 · outbound

This paper cites Hence, a predefined mixing of α or other strategies is a convenient choice.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Hence, a predefined mixing of α or other strategies is a convenient choice

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.750928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5099240b-4f8e-49c8-99a9-5f395790e593 · outbound

This paper cites In this case it pays to use the training data to determine the optimal combination of evolution- ary strategies, but customize γ(0) according to e.g.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization In this case it pays to use the training data to determine the optimal combination of evolution- ary strategies, but customize γ(0) according to e.g

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.741415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5aa4c933-361b-4a61-a31f-891f80ef37d2 · outbound

This paper cites Yang, Optimization algorithms (1970) pp.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Yang, Optimization algorithms (1970) pp

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.731500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4d1977f5-f1b7-40eb-b86c-57792e28f79c · outbound

This paper cites an unresolved cited work.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:22:24.721818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 64429d06-d3fd-413f-95ab-dcb2813f8316 · outbound

This paper cites Deb, Multi-Objective Optimization Using Evolution- ary Algorithms (John Wiley & Sons, Inc., USA, 2001).

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Deb, Multi-Objective Optimization Using Evolution- ary Algorithms (John Wiley & Sons, Inc., USA, 2001)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.712330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f45a556c-129f-4294-b5f3-5ec46f4166e1 · outbound

This paper cites Friedrich, T.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Friedrich, T

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.702471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9a2168b4-f201-4443-b7c1-ca010509b275 · outbound

This paper cites an unresolved cited work.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Unresolved cited work

Reference 8

Resolution: malformed identifier; no resolver link, observed 2026-08-09T21:22:24.424073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16c57e80-98ba-423f-8a67-c216f57374ef · outbound

This paper cites LLM Inference Unveiled: Survey and Roofline Model Insights.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization LLM Inference Unveiled: Survey and Roofline Model Insights

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T21:22:24.427154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31bec9de-fe39-4881-8c76-58215bc0926e · outbound

This paper cites The argumentation follows the- ories in solid state physics which aim to compute proper- ties of materials without external parameters [21, 23, 24].

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization The argumentation follows the- ories in solid state physics which aim to compute proper- ties of materials without external parameters [21, 23, 24]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.692682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8bdb991a-8424-4ae9-96da-79b2a0b92f86 · outbound

This paper cites Maynard Smith and G.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Maynard Smith and G

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.683011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8974b0df-5832-457d-b436-a99dd1420b72 · outbound

This paper cites Hofbauer and K.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Hofbauer and K

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.673489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1eaa7648-97d7-43d1-a4e3-603f451f1bf0 · outbound

This paper cites De Jong, D.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization De Jong, D

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.664287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d610822-f016-4e31-9b14-90720e254268 · outbound

This paper cites Galv´ an and P.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Galv´ an and P

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.654991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e74ec8de-c767-4f21-a8e9-f7d7991f1174 · outbound

This paper cites Maynard Smith, The theory of games and the evolu- tion of animal conflicts, Journal of Theoretical Biology 47, 209 (1974).

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Maynard Smith, The theory of games and the evolu- tion of animal conflicts, Journal of Theoretical Biology 47, 209 (1974)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.645637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0dbe54c-cdbf-4a7c-9da4-a771c1056ea6 · outbound

This paper cites an unresolved cited work.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T21:22:24.451116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4e1e5e0-3552-486f-acc1-6d89b7a2c171 · outbound

This paper cites Axelrod, The Evolution of Cooperation (Basic, New York, 1984).

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Axelrod, The Evolution of Cooperation (Basic, New York, 1984)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.635861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a73f391-9a03-4203-99b8-45b2593e42db · outbound

This paper cites Berger, Fictitious play in 2 × n games, Journal of Economic Theory 120, 139 (2005).

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Berger, Fictitious play in 2 × n games, Journal of Economic Theory 120, 139 (2005)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.625637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bef6cec3-d308-411b-b91e-2902626ac743 · outbound

This paper cites Dawkins, The Selfish Gene: 30Th Anniversary edition (Oxford University Press, London, England, 2006).

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Dawkins, The Selfish Gene: 30Th Anniversary edition (Oxford University Press, London, England, 2006)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.614366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 89785761-5f64-438e-8667-96c6335ef4ab · outbound

This paper cites In LDA, one neglects (some) correlations by replacing complex electronic orbitals by a single func- tion, the electronic density.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization In LDA, one neglects (some) correlations by replacing complex electronic orbitals by a single func- tion, the electronic density

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.604545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f722204f-2a2f-4837-b301-daf87d7ed2f5 · outbound

This paper cites an unresolved cited work.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:22:24.594197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70a08184-9769-4d4e-9a53-34e2720f3e67 · outbound

This paper cites an unresolved cited work.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:22:24.584509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1bf4cf7c-25d7-4a65-bfb3-882753c84565 · outbound

This paper cites an unresolved cited work.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:22:24.574194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 25e6cbc9-7b14-4336-be90-98e885b3e5e6 · outbound

This paper cites Kohn and L.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Kohn and L

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T21:22:24.476237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1883366a-002f-4b35-90f7-934c44865cd5 · outbound

This paper cites 3, represents an example for a weighted approach as e.g.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization 3, represents an example for a weighted approach as e.g

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:22:24.556177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a247134e-ce10-4f59-bb4f-5f31c2ccf85f · outbound

This paper cites Hohenberg and W.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Hohenberg and W

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T21:22:24.482161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6afd5dbd-0073-4c60-8874-15da64cd8f45 · outbound

This paper cites an unresolved cited work.

Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:22:24.537110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 39c8204f-2821-413a-bea9-03e37d3edbec · inbound

Feature weighting for data analysis via evolutionary simulation cites this paper.

Feature weighting for data analysis via evolutionary simulation Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-17T23:30:29.094946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ae4cac0-b4f2-418c-bcfa-7a8b47471d4e · inbound

Feature weighting for data analysis via evolutionary simulation cites this paper.

Feature weighting for data analysis via evolutionary simulation Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T23:23:09.560026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9f734a6-469c-47f4-9d71-2cba731bfaa8 · inbound

Evolutionary Data Theory: On the Similarities between Data Problems and Evolutionary Games cites this paper.

Evolutionary Data Theory: On the Similarities between Data Problems and Evolutionary Games Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T16:25:49.867686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.