Paper Citation Record · LEDGER

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions

As of 15 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2605.22374.

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

pith.paper-citation-record.v1
2605.22374 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T02:04:29.369449Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T12:25:44.070417Z

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

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Outbound references

Observation c151aa23-6b19-4e95-b7b7-52671a2a6b39 · outbound

This paper cites Koza , isbn =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Koza , isbn =

Reference 1

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Observation febf2e8c-3489-4f4b-96d2-af0f273702d4 · outbound

This paper cites Contemporary Symbolic Regression Methods and their Relative Performance , volume =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Contemporary Symbolic Regression Methods and their Relative Performance , volume =

Reference 2

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This paper cites and de Fran.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and de Fran

Reference 3

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This paper cites title =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions title =

Reference 4

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Observation 9a2e4f17-2be3-4565-9f74-42ec5c6c00fc · outbound

This paper cites Time for a Change: a Tutorial for Comparing Multiple Classifiers Through.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Time for a Change: a Tutorial for Comparing Multiple Classifiers Through

Reference 5

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Observation c0919468-16ba-47e7-848e-6aab5081913a · outbound

This paper cites Improving Genetic Programming for Symbolic Regression with Equality Graphs , year =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Improving Genetic Programming for Symbolic Regression with Equality Graphs , year =

Reference 6

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This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 7

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Observation a63dd988-9c89-4218-9fe0-8afab62198cc · outbound

This paper cites Discovering physical laws with parallel symbolic enumeration.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Discovering physical laws with parallel symbolic enumeration

Reference 8

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This paper cites and Affenzeller, Michael , year =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Affenzeller, Michael , year =

Reference 9

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Friedman , title =

Reference 10

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Observation 72e42dc3-8ca9-4cdb-90b4-1fce1db1e59c · outbound

This paper cites Neural Computation , author =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Neural Computation , author =

Reference 11

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This paper cites and Miranda, Manuel and Pallarès, Jordi and Sales-Pardo, Marta , year =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Miranda, Manuel and Pallarès, Jordi and Sales-Pardo, Marta , year =

Reference 12

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This paper cites Predicting friction system performance with symbolic regression and genetic programming with factor variables , DOI =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Predicting friction system performance with symbolic regression and genetic programming with factor variables , DOI =

Reference 13

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This paper cites and Smits, Guido F.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Smits, Guido F

Reference 14

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This paper cites Evolutionary Computation in the Chemical Industry.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Evolutionary Computation in the Chemical Industry

Reference 15

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions 2012 , publisher=

Reference 16

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions 2003 , publisher=

Reference 17

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions 1993 , publisher=

Reference 18

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This paper cites Kilpatrick and M.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Kilpatrick and M

Reference 19

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Observation 6f7a8783-7823-4bcd-b665-5dc6e5e65c92 · outbound

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Computational statistics & data analysis , volume=

Reference 20

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions 2017 , publisher=

Reference 21

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions A Numerical Approach to Genetic Programming for System Identification , year=

Reference 22

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Proceedings of the Genetic and Evolutionary Computation Conference , publisher =

Reference 23

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions year =

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions year =

Reference 25

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions IEEE Transactions on Evolutionary Computation , volume=

Reference 26

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Bayesian optimization for choice data

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This paper cites On the functional form of the radial acceleration relation , volume =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions On the functional form of the radial acceleration relation , volume =

Reference 28

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions 2025 , eprint=

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Comparative Analysis of Model Selection Criteria for Symbolic Regression Using Genetic Programming , ISBN =

Reference 30

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions AJ Alvero, Jinsook Lee, Alejandra Regla-Vargas, Rene Kizil ec, Thorsten Joachims, and Anthony Lis- ing Antonio

Reference 31

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Estimating the Dimension of a Model , volume =

Reference 32

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions The Elements of Statistical Learning , year =

Reference 33

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing , volume =

Reference 34

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions year =

Reference 35

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Effects of reducing redundant parameters in parameter optimization for symbolic regression using genetic programming , volume =

Reference 36

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Information geometry for multiparameter models: new perspectives on the origin of simplicity , volume =

Reference 37

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Reference 38

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Pratap, A

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions 2015 , publisher=

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis , journal =

Reference 41

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Mangili, F

Reference 42

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Proceedings of the Genetic and Evolutionary Computation Conference Companion , pages =

Reference 44

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Kotanchek, Mark

Reference 45

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions title =

Reference 46

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Symbolic regression via

Reference 48

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Structural Risk Minimization-Driven Genetic Programming for Enhancing Generalization in Symbolic Regression , volume =

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-22T02:04:31.101057Z

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This paper cites Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation , DOI =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation , DOI =

Reference 50

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This paper cites and Alonso, C\'.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Alonso, C\'

Reference 51

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This paper cites title =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions title =

Reference 52

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This paper cites 2015 , eprint=.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions 2015 , eprint=

Reference 53

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This paper cites year =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions year =

Reference 54

Resolution: verified exact; doi, observed 2026-05-22T02:04:31.095740Z

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This paper cites User-friendly Introduction to.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions User-friendly Introduction to

Reference 55

Resolution: verified exact; doi, observed 2026-05-22T02:04:31.142648Z

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This paper cites an unresolved cited work.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Unresolved cited work

Reference 56

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Quarterly of Applied Mathematics , year=

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:04:31.773030Z

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This paper cites title =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions title =

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:04:31.765030Z

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This paper cites Toward an artificial intelligence physicist for unsupervised learning , volume =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Toward an artificial intelligence physicist for unsupervised learning , volume =

Reference 59

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Proceedings of the 34th International Conference on Neural Information Processing Systems , articleno =

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:04:31.868753Z

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions and Kammerer, Lukas , year =

Reference 61

Resolution: verified exact; doi, observed 2026-05-22T02:04:31.104208Z

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Observation f02b5e88-eba0-418c-9594-872cdd91c584 · outbound

This paper cites Journal of Machine Learning Research , year =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Journal of Machine Learning Research , year =

Reference 62

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This paper cites Probabilistic Incremental Program Evolution , volume =.

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Probabilistic Incremental Program Evolution , volume =

Reference 63

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Bayesian Machine Scientist to Compare Data Collapses for the Nikuradse Dataset , volume =

Reference 64

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Observation 737b7443-d7b7-45f6-8083-8a50b1ff12b6 · outbound

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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Parameter identification for symbolic regression using nonlinear least squares , volume =

Reference 65

Resolution: verified exact; doi, observed 2026-05-22T02:04:31.006338Z

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Pith citing papers

Observation 44391bb9-e58f-439c-bfe9-2dbf168a7a72 · inbound

Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression cites this paper.

Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions

Reference 13

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