Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Thinking Outside the Template with Modular GP-GOMEA

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Modular GP-GOMEA represents a symbolic regression model as multiple small trees that call each other hierarchically, and the paper shows this generally outperforms single-template GP-GOMEA in $R^2$ while recovering modular ground-truth…

desk verdict Strong synthetic results for a genuinely modular GP representation, but the paper's own discussion contradicts its real-world performance claim. read the letter →

arxiv 2505.01262 v1 pith:KSS6372M submitted 2025-05-02 cs.NE

Joe Harrison , Peter A.N. Bosman , Tanja Alderliesten This is my paper · ORCID
classification cs.NE
keywords symbolicregressiongeneticprogrammingGP-GOMEAmodularityautomaticallydefinedfunctionshierarchicalsubexpressionslinkagelearninginterpretability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Modular GP-GOMEA is a symbolic regression method that replaces the single fixed tree template used by GP-GOMEA with a vector of small tree templates, letting later trees call earlier trees as reusable subexpressions. The paper claims that this representation generally achieves higher $R^2$ than single-template GP-GOMEA, recovers known modular ground-truth expressions more often and faster, and keeps each subexpression small enough to interpret on its own. The reason this matters is that symbolic regression has long traded accuracy against interpretability; if a large effective expression can be built from small reused parts, the trade-off shifts. The paper reports gains on five synthetic and five real-world datasets, while noting that part of the gain comes from extra structure rather than from functional reuse.

What carries the argument

The central object is the multi-tree template individual: a vector of fixed-size tree templates in which only the last tree is the output, while earlier trees can be called as functions through subexpression nodes, and argument nodes tie the caller's inputs to the called tree's parameters. Search proceeds by learning a separate linkage-tree Family of Subsets for each tree, flattening the per-tree FOSes into one shuffled FOS, and applying gene-pool optimal mixing with donor solutions; whole-subexpression swaps are allowed for non-output trees while the output tree never swaps wholesale. This machinery lets a small fixed template per subexpression produce a larger effective expression through hierarchical calls.

What would settle it

Run both methods on a set of problems whose ground truths contain no reusable subexpressions, giving each method the same total node budget and a per-dataset tuned population size; if modular GP-GOMEA does not match or beat single-template GP-GOMEA in $R^2$ and recovery rate on most of those problems, the central claim is falsified.

Watch

Extended reading notes

Core claim

On its own terms, this paper establishes that expressing a candidate symbolic regression model as multiple hierarchically callable fixed-size trees improves both accuracy and recovery speed over GP-GOMEA's single fixed template. In the proposed multi-tree representation, only the last tree is the output; any earlier tree can appear as a subexpression node in a later tree, with argument nodes passing the caller's inputs into the called tree, and cycles are prevented by allowing tree $i$ to call only trees $j<i$. Across five synthetic datasets whose ground truths reuse subexpressions, the modular configuration reaches higher $R^2$ and recovers the exact expression more often than single-template GP-GOMEA and a template-constrained GP baseline, and it does so in less wall-clock time per generation. On five real-world datasets, the modular configuration achieves higher $R^2$ as the number of trees and their depth grow, though the paper's own analysis shows that subexpression reuse as functions is less frequent than expected and that much of the benefit is attributable to having more available structure.

Load-bearing premise

The claim that modular GP-GOMEA generally outperforms single-template GP-GOMEA rests on a comparison in which every synthetic ground truth is built from reusable subexpressions and the real-world runs use a population size tuned on only two of the synthetic problems, so the advantage may not carry over to non-modular problems or to a tuned baseline.

Editorial extensions

If this is right

  • Symbolic regression models no longer need their final shape fixed in advance; a deep or unbalanced expression can be assembled from shallow templates, so the user does not have to guess the right template depth or width.
  • On problems with genuinely reusable subexpressions, the method should find exact or near-exact expressions faster, which directly supports scientific discovery settings where the goal is to recover a known law.
  • The multi-tree representation creates a natural unit for human inspection: a reader can understand a small subexpression once and then treat it as a feature wherever it is reused.
  • Increasing the number of trees or template depth improves $R^2$ up to a point, after which the larger individual slows generation throughput within a fixed time budget; future configurations should be chosen with the time budget in mind.
  • Even without parsimony pressure, runs on real-world datasets show at least one reused subexpression on average, indicating that some real regression problems are decomposable in a way this representation can capture.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A sharper test of the representation would hold total node capacity fixed across modular and single-template variants; the paper's data suggest the advantage could shrink when the single template is given as many nodes.
  • On non-modular ground truths, modular GP-GOMEA may lose its edge; the synthetic benchmarks are modular by construction, so the 'generally outperforms' claim should be read as limited to problems with reusable structure.
  • Adding parsimony pressure aimed at subexpression reuse, for instance by making reuse an archive objective, is a plausible next step the paper identifies as future work; it could turn rare functional reuse into a systematic behavior.
  • The qualitative subexpression analysis could support a user study: if humans understand expressions with reused subexpressions faster and more accurately than flat expanded expressions, the interpretability argument would have direct evidence.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces Modular GP-GOMEA, a symbolic-regression variant in which an individual is a vector of fixed-size tree templates, with later trees allowed to call earlier trees as subexpressions through newly introduced subexpression and argument nodes. The central claims are that this representation generally outperforms single-template GP-GOMEA in R2 and that on synthetic datasets with reusable subexpressions it recovers ground-truth expressions faster than the non-modular baseline. The paper reports experiments on five synthetic datasets deliberately constructed from reusable subexpressions and five real-world datasets, along with population-size, template-depth, and tree-count studies, plus a qualitative interpretability example on the Bike Daily dataset. The synthetic recovery results are internally consistent, but the real-world generalization claim is contradicted by the paper's own Discussion and is supported only by averaged results without per-dataset statistics or significance tests.

Significance. If the claims were fully supported, the modular representation would be a meaningful extension of GP-GOMEA: it allows hierarchical subexpression reuse while retaining small fixed templates, and the synthetic experiments show substantially higher ground-truth recovery rates than the single-template baseline. The paper also contributes a set of synthetic benchmarks with known modular ground truths and a useful qualitative analysis of an evolved subexpression. These are real strengths. However, the headline claim of 'generally outperforms' is not established by the evidence as presented: the real-world comparison rests on aggregated R2 averages with no paired testing, and the authors themselves state that the benefit primarily comes from a more flexible template structure rather than from functional reuse. The paper would be significantly stronger if the claims were narrowed to the synthetic regime and if the real-world results were reported per dataset with appropriate statistical analysis.

major comments (4)
  1. [Abstract, Section 4, Section 5, Figure 8b] The Abstract's claim that Modular GP-GOMEA 'generally outperforms single-template GP-GOMEA' and Section 5's statement that it 'achieved higher R2 across five real-world datasets' are directly contradicted by Section 4, which states that 'Modular GP-GOMEA performs on par with standard GP-GOMEA on real-world datasets.' The only real-world evidence is Figure 8b, which shows averages over five datasets with standard-deviation whiskers; no per-dataset R2 table and no paired significance test are provided. Given that the real-world datasets are central to the generality claim, the authors must either provide per-dataset paired statistics or revise the abstract and conclusions to match the more modest Discussion statement.
  2. [Section 3.3, Section 3.5, Discussion] The population size used for the real-world comparisons in Section 3.5 is 4096, chosen because it was the best-tried modular size under a 1-hour budget for synthetic problems, as shown in Figure 4 and acknowledged in the Discussion: it was 'the best-tested one for 2 out of 5 synthetic problems and was not tested for real-world problems.' This is a potential bias against the non-modular baseline, because Figure 3 shows that non-modular GP-GOMEA's average R2 has a general upward trend with population size up to 32768. To support a fair comparison, the authors should either tune the population size for each configuration and dataset or demonstrate that the qualitative conclusion is insensitive to this choice.
  3. [Section 3.2, Section 3.5] All five synthetic ground truths are deliberately constructed from reusable subexpressions, so the modular representation is given an advantage by construction on these benchmarks. This is reasonable for demonstrating the mechanism, but it does not support the broad 'generally outperforms' claim. Moreover, the paper's own analysis in Section 3.5 states that the 4x2 configuration, which has sufficient structure to represent synthetic expression 1, attains R2=0.68 versus 0.87 for 4x4 and 0.82 for 7x1, and that this 'indicates that subexpression re-use alone does not explain the performance gap.' This is an honest caveat, but it means the paper has not isolated the contribution of modular reuse from the contribution of a larger or differently shaped template. An ablation that controls total template nodes, search effort, or both is needed before claiming that the modular representation itself is the source of the improvement.
  4. [Section 3.1, Figure 8] No statistical significance tests are reported anywhere in the paper. The claims of superiority rest on average R2 values with standard-deviation whiskers, and for the real-world results in Figure 8b the whiskers overlap substantially across configurations. A paired test over the 30 repetitions, or at least a per-dataset table with confidence intervals, is necessary to support 'generally outperforms' and 'achieved higher R2.' Without this, the observed differences could be within run-to-run noise.
minor comments (5)
  1. [Section 2.1] The text mentions 'concical tree initialization methods'; this appears to be a typo, likely for 'canonical' or 'conical'.
  2. [Section 3.3] In the paragraph on population size, 'non-modular configuration of GP-GOMEOA' should read 'GP-GOMEA'.
  3. [Figure 7 caption] The caption contains typos: 'indivdiual' should be 'individual' and 'evalutations' should be 'evaluations'.
  4. [Section 3.5, Table 3] The distinction between 'subexpressions used' and 'subexpressions re-used' is not defined explicitly before the table; a short definition or a pointer to Section 2.2 would improve clarity.
  5. [Appendix A, Figure 11] The appendix reports R2 with and without inputs in the subexpression operator set only as a figure; providing the per-dataset numerical values would make the claim more reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the comparisons are empirical, and the admitted benchmark limitations concern validity, not self-referential reasoning.

full rationale

The paper's central claims are empirical comparisons between Modular GP-GOMEA and single-template GP-GOMEA, not derivations from fitted inputs. The modular representation is defined independently of the R2 outcomes; the synthetic ground truths are known expressions, and recovering them is a falsifiable test rather than an identity. The only fitted configuration choice is the population size, and the paper explicitly discloses that 4096 was chosen from synthetic problems and not tuned for real-world data (Section 4); this is a limitation in generalization, not a case of a fitted parameter being renamed a prediction. Self-citations to prior GP-GOMEA work [24,25] supply implementation details and baselines, but the experiments are run here against those baselines, so the comparison does not reduce to the cited work. The abstract's 'generally outperforms' and the Discussion's 'performs on par' are inconsistent, and Figure 8b reports only averaged real-world R2 without per-dataset evidence; these are correctness and evidence concerns outside the circularity checklist. No equation or construction in the paper equates the claimed result with an input by definition, so no circular step is exhibited.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The central claim is an empirical algorithmic comparison, so the ledger mainly contains experimental design choices and domain assumptions rather than fitted physical parameters. The most consequential choices are the population size, the 4x4 template configuration, the arity limit, and the fixed time budgets, all of which affect whether the observed advantage reflects the modular representation itself or simply a more favorable search configuration. The invented entities are internal algorithmic components, not physical postulates.

free parameters (4)
  • Population size in depth and tree-count experiments = 4096
    Used for both synthetic and real-world comparisons in Section 3.5; chosen as the best-tried size for only 2 of 5 synthetic problems and not tuned for real-world data, as the authors acknowledge in the Discussion.
  • Main modular configuration: number of trees and tree depth = 4 trees, depth 4
    The 4x4 configuration is the primary modular setting; chosen to provide enough structure to represent the synthetic expressions, and the paper shows that some performance gain comes from having more available template structure rather than from functional reuse alone.
  • Maximum template arity = 2
    The paper limits all tree templates to arity 2, which constrains subexpression and argument nodes to two arguments and affects the expressiveness and search space.
  • Fixed time budget = 6 hours for population-size experiments, 1 hour for depth experiments
    The fixed budget can favor certain configurations and is explicitly discussed as a limitation; a time-to-optimum comparison would be a more comprehensive evaluation.
assumptions (4)
  • standard math UPGMA hierarchical clustering with mutual information estimates produces a valid linkage tree FOS for GP-GOMEA.
    The method relies on this clustering to group dependent loci; it assumes MI estimates computed from finite populations are reliable enough to guide GOM.
  • domain assumption Expression size correlates with interpretability, and smaller subexpressions reduce cognitive load.
    The interpretability motivation rests on this claimed link, which is cited from prior work but not empirically established for subexpression reuse in this paper; the authors explicitly state that no user study supports this intuition.
  • domain assumption Synthetic ground truths containing reusable subexpressions are representative of problems where modularity helps.
    All five synthetic datasets are constructed with repeated subexpressions, so the strong synthetic results may reflect benchmark construction rather than general SR difficulty.
  • ad hoc to paper The ordering restriction j < i for tree calls prevents cycles and is sufficient to cover useful modular structures.
    The paper imposes this design constraint to avoid infinite loops, but it may exclude some valid hierarchical dependency structures that a less constrained representation would allow.
invented entities (2)
  • Subexpression node
    purpose: A function node that lets a tree call an earlier tree as a function and use its output.
    This is a new algorithmic primitive introduced in the paper; its utility is demonstrated only through the experiments reported here, with no external falsifiable handle.
  • Argument node
    purpose: A terminal node that points to an argument of the calling tree, enabling a subexpression to be called with different inputs.
    This is a second new algorithmic primitive; it supports functional reuse but has no independent evidence outside the presented experiments.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Thinking Outside the Template with Modular GP-GOMEA." pith.science (2026). https://pith.science/paper/KSS6372M

@misc{pith2026250501262,
  author       = {Pith},
  title        = {Pith review of: Thinking Outside the Template with Modular GP-GOMEA},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KSS6372M}},
  note         = {Machine review of arXiv:2505.01262}
}
read the original abstract

The goal in Symbolic Regression (SR) is to discover expressions that accurately map input to output data. Because often the intent is to understand these expressions, there is a trade-off between accuracy and the interpretability of expressions. GP-GOMEA excels at producing small SR expressions (increasing the potential for interpretability) with high accuracy, but requires a fixed tree template, which limits the types of expressions that can be evolved. This paper presents a modular representation for GP-GOMEA that allows multiple trees to be evolved simultaneously that can be used as (functional) subexpressions. While each tree individually is constrained to a (small) fixed tree template, the final expression, if expanded, can exhibit a much larger structure. Furthermore, the use of subexpressions decomposes the original regression problem and opens the possibility for enhanced interpretability through the piece-wise understanding of small subexpressions. We compare the performance of GP-GOMEA with and without modular templates on a variety of datasets. We find that our proposed approach generally outperforms single-template GP-GOMEA and can moreover uncover ground-truth expressions underlying synthetic datasets with modular subexpressions at a faster rate than GP-GOMEA without modular subexpressions.

Figures

Figures reproduced from arXiv: 2505.01262 by the authors.

Figure 1
Figure 1. Example of an FOS and a GOM swap. The red rectangle indicates an FOS [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Example of an individual with three trees [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Comparison of average R2 between different configurations of GP￾GOMEA (4 trees of depth 4, 1 tree of depth 7) and depth-constrained GP. Positions of the markers of the GP configurations are shifted sideways for clar￾ity. In [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The population size with the highest average [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Comparison between the ability of different configurations of GP-GOMEA [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 7
Figure 7. Figure 7: Comparison in average time per individual per generation, average dura [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Average R2 of best individual per tree depth and per number of multi￾trees. Whiskers indicate the standard deviation range. Figure 8a shows a clear trend: increasing the number of multi-trees in GP￾GOMEA and the depth of their templates, generally results in higher R2 …
Figure 9
Figure 9. Figure 9: Average # nodes per tree depth and number of multi-trees. Whiskers [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Plots of subexpression obtained with Modular GP-GOMEA. The ran [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]
Figure 11
Figure 11. Figure 11: R2 with feature inputs in subexpression operator set and without (Koza’s formulation) [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Better Multi-Objective GP-GOMEA -- But do we Need it?

    cs.NE 2025-07 conditional novelty 6.0 of 10

    For accuracy vs expression size, single-objective GP-GOMEA with an archive-only multi-objective log outperforms true multi-objective GP-GOMEA, despite new clustering improvements.

Reference graph

Works this paper leans on

26 extracted references · 22 canonical work pages · cited by 1 Pith paper

  1. [1]

    Asuncion, A., Newman, D.: Uci machine learning repository (2007)

    2007

  2. [2]

    arXiv preprint arXiv:1702.08608 (2017)

    Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learn- ing. arXiv preprint arXiv:1702.08608 (2017)

    arXiv 2017

  3. [3]

    In: 2016 IEEE Congress on Evolutionary Computation (CEC)

    Gerules, G., Janikow, C.: A survey of modularity in genetic programming. In: 2016 IEEE Congress on Evolutionary Computation (CEC). pp. 5034–5043. IEEE (2016)

    work page 2016

  4. [4]

    right to explanation

    Goodman, B., Flaxman, S.: European union regulations on algorithmic decision- making and a “right to explanation”. AI magazine 38(3), 50–57 (2017)

    work page 2017

  5. [5]

    Information processing letters 104(6), 205–210 (2007)

    Gronau, I., Moran, S.: Optimal implementations of upgma and other common clustering algorithms. Information processing letters 104(6), 205–210 (2007)

    work page 2007

  6. [6]

    In: International Conference on Parallel Problem Solving from Nature

    Harrison, J., Alderliesten, T., Bosman, P.A.: Gene-pool optimal mixing in cartesian genetic programming. In: International Conference on Parallel Problem Solving from Nature. pp. 19–32. Springer (2022)

    work page 2022

  7. [7]

    In: European Conference on Genetic Programming

    Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scal- ing. In: European Conference on Genetic Programming. pp. 70–82. Springer (2003)

    work page 2003

  8. [8]

    Kommenda, M., Affenzeller, M.: Local optimization and complexity control for symbolic regression. Ph. D. dissertation, Ph. D. thesis (2018)

    work page 2018

Show all 26 references
  1. [9]

    Genetic Programming and Evolvable Machines 21(3), 471–501 (2020) 22 Harrison et al

    Kommenda, M., Burlacu, B., Kronberger, G., Affenzeller, M.: Parameter identifi- cation for symbolic regression using nonlinear least squares. Genetic Programming and Evolvable Machines 21(3), 471–501 (2020) 22 Harrison et al

    2020

  2. [10]

    In: Foundations of Genetic Algorithms, vol

    Koza, J.R.: Hierarchical automatic function definition in genetic programming. In: Foundations of Genetic Algorithms, vol. 2, pp. 297–318. Elsevier (1993)

    1993

  3. [11]

    Statistics and computing 4, 87–112 (1994)

    Koza, J.R.: Genetic programming as a means for programming computers by nat- ural selection. Statistics and computing 4, 87–112 (1994)

    1994

  4. [12]

    Advances in Genetic Programming pp

    Koza, J.R., Kinner, K., et al.: Scalable learning in genetic programming using au- tomatic function definition. Advances in Genetic Programming pp. 99–117 (1994)

    1994

  5. [13]

    arXiv preprint arXiv:2107.14351 (2021)

    La Cava, W., Orzechowski, P., Burlacu, B., de Fran¸ ca, F.O., Virgolin, M., Jin, Y., Kommenda, M., Moore, J.H.: Contemporary symbolic regression methods and their relative performance. arXiv preprint arXiv:2107.14351 (2021)

    2021 arXiv

  6. [14]

    Springer (1998)

    Langdon, W.B., Poli, R.: Fitness causes bloat. Springer (1998)

    1998

  7. [15]

    Queue 16(3), 31–57 (2018)

    Lipton, Z.C.: The mythos of model interpretability: In machine learning, the con- cept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)

    2018

  8. [16]

    In: Parallel Problem Solving from Nature-PPSN XII: 12th International Conference, Taormina, Italy, September 1-5, 2012, Pro- ceedings, Part II 12

    Luong, H.N., Bosman, P.A.: Elitist archiving for multi-objective evolutionary algo- rithms: To adapt or not to adapt. In: Parallel Problem Solving from Nature-PPSN XII: 12th International Conference, Taormina, Italy, September 1-5, 2012, Pro- ceedings, Part II 12. pp. 72–81. S...

    2012

  9. [17]

    IEEE Transactions on Evolutionary Computation 27(3), 621–641 (2022)

    Mei, Y., Chen, Q., Lensen, A., Xue, B., Zhang, M.: Explainable artificial intel- ligence by genetic programming: A survey. IEEE Transactions on Evolutionary Computation 27(3), 621–641 (2022)

    2022

  10. [18]

    In: Proceedings of the 22nd ACM SIGKDD interna- tional conference on knowledge discovery and data mining

    Ribeiro, M.T., Singh, S., Guestrin, C.: ” why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD interna- tional conference on knowledge discovery and data mining. pp. 1135–1144 (2016)

    2016

  11. [19]

    arXiv preprint arXiv:1708.08296 (2017)

    Samek, W.: Explainable artificial intelligence: Understanding, visualizing and in- terpreting deep learning models. arXiv preprint arXiv:1708.08296 (2017)

    2017 arXiv

  12. [20]

    arXiv preprint arXiv:2402.09854 (2024)

    Schlender, T., Malafaia, M., Alderliesten, T., Bosman, P.A.: Improving the ef- ficiency of gp-gomea for higher-arity operators. arXiv preprint arXiv:2402.09854 (2024)

    2024 arXiv

  13. [21]

    science 324(5923), 81–85 (2009)

    Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. science 324(5923), 81–85 (2009)

    2009

  14. [22]

    In: Proceed- ings of the 13th annual conference on Genetic and evolutionary computation

    Thierens, D., Bosman, P.A.: Optimal mixing evolutionary algorithms. In: Proceed- ings of the 13th annual conference on Genetic and evolutionary computation. pp. 617–624 (2011)

    2011

  15. [23]

    In: Proceedings of the Genetic and Evolutionary Computation Con- ference

    Virgolin, M., Alderliesten, T., Witteveen, C., Bosman, P.A.: Scalable genetic pro- gramming by gene-pool optimal mixing and input-space entropy-based building- block learning. In: Proceedings of the Genetic and Evolutionary Computation Con- ference. pp. 1041–1048 (2017)

    2017

  16. [24]

    Evolu- tionary computation 29(2), 211–237 (2021)

    Virgolin, M., Alderliesten, T., Witteveen, C., Bosman, P.A.: Improving model- based genetic programming for symbolic regression of small expressions. Evolu- tionary computation 29(2), 211–237 (2021)

    2021

  17. [25]

    In: Proceedings of the Genetic and Evolutionary Computation Conference Companion

    Virgolin, M., Bosman, P.A.: Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 2289–2297 (2022)

    2022

  18. [26]

    Walker, J.A., Miller, J.F.: The automatic acquisition, evolution and reuse of mod- ules in cartesian genetic programming. IEEE Transactions on Evolutionary Com- putation 12(4), 397–417 (2008) Thinking Outside the Template with Modular GP-GOMEA 23 A Adding extra inputs In the o...

    2008

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.