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REVIEW 3 major objections 5 minor 54 references

Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read GAPA, a genetic-algorithm acceleration framework for perturbed substructure optimization, rewrites the GA loop as matrix operations and claims average speedups over the Evox baseline of about 4x, with solution quality preserved.

desk verdict A useful GPU-acceleration framework for GA-based PSSO with a credible speedup story, but the 'retained quality' half of the claim is unsupported and the paper mixes 4x and 2x speedup numbers. read the letter →

arxiv 2412.20980 v1 pith:TJGPCX4Y submitted 2024-12-30 cs.NE cs.SI

Shanqing Yu , Meng Zhou , Jintao Zhou , Minghao Zhao , Yidan Song , Yao Lu , Zeyu Wang , Qi Xuan This is my paper · ORCID
classification cs.NEcs.SI
keywords geneticalgorithmGPUaccelerationperturbedsubstructureoptimizationgraphminingparallelcomputingevolutionarycomputationnetworkattackanddefensefitnessfunctiondesign
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

Perturbed substructure optimization (PSSO) searches over nearly endless ways to add or delete edges and nodes to change what a graph-mining algorithm outputs. Genetic algorithms are a standard search method for PSSO, but each generation is slow because every individual mutation is evaluated sequentially. The paper argues that the entire GA loop can be rebuilt as matrix arithmetic: populations become matrices, crossover and mutation become masked row operations, elitism becomes a sort, and fitness becomes batched adjacency-matrix updates. It packages this as GAPA, a library covering ten published PSSO algorithms across four graph-mining tasks, and reports large speedups on real datasets. The authors claim, in the abstract, nearly 4x average acceleration over the Evox baseline while keeping solution quality; the detailed Evox comparison in Section IV.F reports a 2x overall acceleration.

What carries the argument

The load-bearing object is the population matrix $POP \in \mathbb{R}^{s \times k}$ together with two binary mask matrices, the crossover mask $R_C$ and the mutation mask $R_M$. Crossover is implemented as $P C \odot R_C + P OP \odot \overline{R_C}$, and mutation as $C P OP \odot R_M + R P OP \odot \overline{R_M}$, turning two iterative genetic operators into element-wise tensor operations. Elitism becomes an argsort over the concatenated fitness vectors of the old and mutated populations. The second mechanism is the fitness reconstruction of Eq. (13): $\Delta f(G,p) \approx \hat{F}(\hat{P}(A,p))$, where both the perturbation update and the evaluation are batched on the adjacency matrix $A$, with node perturbations treated as edge perturbations. The SixDST fitness example computes the reachability matrix as $\mathrm{Normalizer}((A+I)^N)$ and uses repeated squaring so the matrix-power cost scales as $O(\log_2 N \cdot n^3)$.

What would settle it

Run an algorithm such as CutOff or TDE with its original iterative fitness function and with GAPA's batched Eq. (13) fitness on the same datasets, holding all GA parameters fixed. If the best perturbed substructures found by the two versions differ materially, or if the reported metrics (Q, NMI, PC(G), ASR, accuracy, AUC) move outside the ranges the paper reports, then the approximation is not negligible for that task.

Watch

Extended reading notes

Core claim

On its own terms, the paper's contribution is that a genetic algorithm for PSSO is not a loop of individual evaluations but a sequence of tensor operations: initialize a population matrix, apply a masked crossover row-shuffle, apply a masked mutation index-replacement, sort by fitness, and repeat. The fitness calculation, normally the bottleneck, is reconstructed so that the iterative index-based perturbation update is replaced by a batched matrix update (Eq. 13) whose output the paper asserts is approximately equal to the traditional result. For the SixDST example, reachability is computed via Normalizer((A+I)^N) with N at least the network diameter, reducing the fitness cost from O(N $n^{3}$) to O(log N $n^{3}$) by repeated squaring. Four acceleration modes (S, SM, M, MNM) distribute these operations across one or many GPUs. The paper reports speedups up to roughly 17x on individual algorithms and claims that this comes without degrading the reported task metrics such as modularity, NMI, PC(G), ASR, accuracy, and AUC.

Load-bearing premise

The load-bearing premise is the paper's assertion in Section III-C (Eq. 13) that replacing the iterative, index-by-index perturbation update with one batched matrix update produces results that are approximately equal to the traditional method, so that solution quality is unchanged; the paper does not prove this equivalence.

Editorial extensions

If this is right

  • Ten existing PSSO algorithms, spanning community detection attacks, critical node detection, node classification attacks, and link prediction attacks, can be run under one unified GA interface.
  • Larger population sizes and larger networks make the acceleration gap over the CPU baseline grow, so the framework is positioned for network-scale experiments that currently time out.
  • The M mode, which distributes genetic operations across processes and exchanges data only when needed, is the best single mode in the paper's experiments, suggesting a default deployment choice.
  • Adding GPUs in the distributed modes accelerates computation up to a saturation point beyond which data-transfer costs dominate; the paper reports this crossover explicitly.
  • For fitness functions that can be expressed as adjacency-matrix batch operations, iteration cost drops from a per-individual loop to a parallel tensor pass.

Reading between the lines

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

  • Because the matrix reconstruction of genetic operators does not depend on the specific task, the same GAPA-style rewriting should apply to other population-based graph perturbation methods, such as differential evolution or estimation-of-distribution algorithms, so the library could grow beyond the ten algorithms listed.
  • A boundary condition is the approximation in Eq. (13): the claimed quality retention has been demonstrated only for objectives whose evaluation can be batched faithfully; for stateful fitnesses such as modularity recomputed after each edge flip, users should measure the approximation error before trusting the reported speedups.
  • If the speedups transfer to production-scale networks, cheap black-box attacks on community detection and node classification become much easier to mount, which sharpens the privacy threat that PSSO defenses need to address.
  • The framework's speedup pattern suggests that PSSO's true cost is fitness evaluation rather than genetic search, so future work should prioritize surrogate or incremental fitness updates.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes GAPA, a PyTorch-based acceleration framework for genetic-algorithm-based perturbed substructure optimization (PSSO) on complex networks. The framework reformulates GA operators (initialization, crossover, mutation, fitness evaluation, elitism) as matrix operations, introduces four acceleration modes (S, SM, M, MNM) on CPU/GPU and distributed settings, and bundles an extensible library covering 10 PSSO algorithms across four graph-mining tasks. Experiments on 12 datasets (listed in Table I) compare CPU and GPU modes, study population-size scaling, evaluate distributed scaling, and compare against the Evox framework. The central claim is that GAPA achieves large speedups, including 'nearly 4x' acceleration over Evox, while retaining solution quality.

Significance. If the claims are substantiated, GAPA would be a practically useful engineering contribution: it addresses a real bottleneck in GA-based PSSO, offers a public repository with concrete implementations of 10 algorithms, and reports speedups on datasets of nontrivial size. The matrix-based reformulation of genetic operators is likely correct and the acceleration numbers are plausible for GPU execution. The main value is in the framework and benchmark suite, not in a new algorithmic insight. The paper's central weakness is that the quality-retention half of the claim is not established: the core approximation in Eq. (13) is unproven, and the reported quality metrics show unexplained discrepancies between CPU and GPU modes. The headline speedup factor is also internally inconsistent between the abstract/introduction and the Evox comparison section.

major comments (3)
  1. [Section III-C, Eq. (13)] The central approximation that the iterative index-based perturbation update/evaluation of Eq. (12) can be replaced by the batched matrix computation of Eq. (13) is asserted with 'approximately equal' and 'negligible' impact, but no proof, error bound, or equivalence argument is provided. This is load-bearing because if the batched fitness landscape differs from the iterative one, GAPA is solving a different optimization problem. The SixDST example illustrates the risk: Eq. (15)-(17) truncate the reachability computation at N=6 without checking whether each network's diameter is at most 6. The paper should either prove an equivalence bound for the classes of fitness functions used, or empirically demonstrate on common seeds that the batched and sequential versions produce identical fitness values for each algorithm. Until then, the 'retaining high-quality solutions' half of the central claim is unsupported.
  2. [Section IV.F vs. Abstract/Introduction] The speedup claim is internally inconsistent. The abstract states 'achieving an average of 4x the acceleration of Evox' and the introduction claims 'nearly 4× further acceleration', but Section IV.F reports that GAPA and Evox reach 6× and 3× acceleration on CDA-EDA, and 20× and 10× on SixDST, and explicitly concludes that 'the overall acceleration effect of GAPA is twice of Evox.' These numbers imply a 2× speedup over Evox, not 4×. The headline claim in the abstract and introduction is therefore not supported by the paper's own reported experiments. The authors should correct the claim or provide the additional experiments that justify 4×.
  3. [Table III] The quality metrics are reported as single-run numbers with no error bars, repeated trials, or significance tests, and several values show unexplained large shifts between CPU and GPU modes. For example, SixDST MCN on ER500 is 156 in CPU mode but 310-319 in every GPU mode, and QAttack NMI on Karate drops from 0.92 (CPU) to 0.40 (M) and 0.38 (MNM). These discrepancies directly undermine the claim that GAPA 'retains high-quality solutions', since a quality improvement of that magnitude suggests the batched fitness landscape differs from the original. The authors should repeat experiments with multiple seeds, report mean±std, and investigate and explain any mode-dependent quality changes.
minor comments (5)
  1. [Abstract and Section IV.A] The abstract states '18 datasets' while Section IV.A says '12 widely used dataset' and Table I lists 12 datasets; the count should be corrected for consistency.
  2. [Section IV.F] The text refers to 'NCA-EDA' but the algorithm being compared is CDA-EDA, as used in Tables I, III, and IV; please fix the typo.
  3. [Section IV.E] The distributed acceleration experiments use datasets Hamilton3000, Hamilton4000, and Powergrid, but these are not introduced in Table I or described anywhere; their sources and properties should be provided.
  4. [Section IV.F] The comparison with Evox does not state the Evox implementation details or parameter settings (e.g., whether the same GA operators, population sizes, and iterations were used), which is needed for reproducibility of the acceleration ratios.
  5. [General] The paper makes several 'first' claims (e.g., 'first to present', 'for the first time') that are difficult to verify and could be softened; the concrete contributions stand on their own.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: GAPA's acceleration claims are benchmarked against external CPU/Evox baselines and no fitted parameter is renamed as a prediction.

full rationale

The paper's central claim is an engineering acceleration result: GAPA re-expresses GA operators (crossover, mutation, elitism, fitness) as matrix/batch operations (Eqs. 2-11) and measures runtime against an unaccelerated CPU implementation and against the external Evox library (Section IV.F, Fig. 7). No parameter is fitted to the measured speedups, and the speedup numbers are not implied by the construction of the framework; they could in principle have come out worse, as the paper itself reports cases where GPU modes are slower on small populations (Section IV.D). The only load-bearing approximations are in the fitness-function design: Eq. (13) asserts that the batched perturbation update is 'approximately equal' to the index-iterative update, and Eq. (15)/(17) truncates the reachability sum at N=6 using the six-degrees heuristic. These are unproven equivalence claims and are a genuine correctness risk -- the paper even admits quality fluctuations in Table III and gives no error bars -- but they are not circular: the approximation is not defined in terms of the conclusion, and no self-citation is used to justify it. The authors' own earlier PSSO algorithms (QAttack, CDA-EDA, SixDST, TDE, etc.) serve as benchmark workloads, and citing one's own algorithms as test cases is not a circularity of argument. The internal inconsistency between the abstract's '4x acceleration' and Section IV.F's 'overall acceleration effect of GAPA is twice of Evox' is a reporting inconsistency, not a self-referential derivation. There is no instance where a fitted input is called a prediction, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation to make the central result forced. The paper is self-contained with respect to its empirical claims, so the circularity score is 0.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the approximation in Eq. (13), the six-degree truncation, standard matrix algebra, and an undirected network model. No fitted parameters are used; the only hand-chosen numerical input is N=6 in the SixDST accessibility calculation.

free parameters (1)
  • truncation length N = 6
    Chosen by hand in Eq. (15) from the six degrees of separation theorem to avoid the full matrix power series; can change approximation error for networks with diameter greater than 6.
assumptions (5)
  • domain assumption Undirected unweighted network model for PSSO with edge and node perturbations representable as adjacency matrix changes.
    Section III-A defines G=(V,E) as undirected and unweighted, and Section III-C treats node and edge perturbations as edge perturbations in the adjacency matrix.
  • ad hoc to paper Fitness functions for GA-based PSSO can be decomposed into a perturbation update function P and an evaluation function F, and can be approximated by batched matrix operations with negligible impact.
    Section III-C states this approximation in Eq. (13) and provides no bound; it is the central premise of the acceleration design.
  • domain assumption Small-world and social networks have diameter at most 6, so Eq. (15) with N=6 approximates the accessibility matrix.
    Section III-C invokes Ref. [37]; no dataset-specific diameter verification or error analysis is reported.
  • domain assumption Genetic operators can be implemented as elementwise mask matrix operations with selection, crossover, mutation, and elitism treated as independent across individuals.
    Section III-B Eqs. (3)-(11); standard GA behavior but relies on no sequential dependency between gene sites.
  • standard math Binomial theorem and matrix multiplication properties allow rewriting the sum of powers as (A+I)^N.
    Eqs. (16)-(17); standard mathematical identity with no extra assumptions.

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Pith. "Pith review of Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network." pith.science (2026). https://pith.science/paper/TJGPCX4Y

@misc{pith2026241220980,
  author       = {Pith},
  title        = {Pith review of: Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TJGPCX4Y}},
  note         = {Machine review of arXiv:2412.20980}
}
read the original abstract

Evolutionary computing, particularly genetic algorithm (GA), is a combinatorial optimization method inspired by natural selection and the transmission of genetic information, which is widely used to identify optimal solutions to complex problems through simulated programming and iteration. Due to its strong adaptability, flexibility, and robustness, GA has shown significant performance and potentiality on perturbed substructure optimization (PSSO), an important graph mining problem that achieves its goals by modifying network structures. However, the efficiency and practicality of GA-based PSSO face enormous challenges due to the complexity and diversity of application scenarios. While some research has explored acceleration frameworks in evolutionary computing, their performance on PSSO remains limited due to a lack of scenario generalizability. Based on these, this paper is the first to present the GA-based PSSO Acceleration framework (GAPA), which simplifies the GA development process and supports distributed acceleration. Specifically, it reconstructs the genetic operation and designs a development framework for efficient parallel acceleration. Meanwhile, GAPA includes an extensible library that optimizes and accelerates 10 PSSO algorithms, covering 4 crucial tasks for graph mining. Comprehensive experiments on 18 datasets across 4 tasks and 10 algorithms effectively demonstrate the superiority of GAPA, achieving an average of 4x the acceleration of Evox. The repository is in https://github.com/NetAlsGroup/GAPA.

Figures

Figures reproduced from arXiv: 2412.20980 by the authors.

Figure 1
Figure 1. Acceleration framework of GAPA in genetic operation. GAPA is an acceleration framework designed to optimize genetic operation, including [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Workflow of SM mode acceleration. Specifically, in this mode, [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Workflow of M mode acceleration. Specifically, in this mode, the pop [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Illustration of the additional computation time introduced by multiple [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 7
Figure 7. Figure 7: The figure illustrates the acceleration effects of CDA-EDA and [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 6
Figure 6. Figure 6: The diagram illustrates the computational acceleration of the SixDST [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

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Reference graph

Works this paper leans on

54 extracted references · 45 canonical work pages

  1. [1]

    Evolutionary computation in medicine: an overview,

    C. A. Pena-Reyes and M. Sipper, “Evolutionary computation in medicine: an overview,”Artificial Intelligence in Medicine, vol. 19, no. 1, pp. 1–23, 2000

    work page 2000

  2. [2]

    Malik, A

    H. Malik, A. Iqbal, P. Joshi, S. Agrawal, F. I. Bakhsh et al. , Meta- heuristic and evolutionary computation: algorithms and applications . Springer, 2021, vol. 916

    work page 2021

  3. [3]

    Designing neural networks through neuroevolution,

    K. O. Stanley, J. Clune, J. Lehman, and R. Miikkulainen, “Designing neural networks through neuroevolution,” Nature Machine Intelligence , vol. 1, no. 1, pp. 24–35, 2019

    2019

  4. [4]

    Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks,

    Z. Liu, D. Yang, Y . Wang, M. Lu, and R. Li, “Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks,” Applied Soft Computing , vol. 135, p. 110040, 2023

    work page 2023

  5. [5]

    A constrained multi-objective evolutionary algorithm with pareto estimation via neural network,

    Z. Liu, P. Zhao, J. Cao, J. Zhang, and Z. Chen, “A constrained multi-objective evolutionary algorithm with pareto estimation via neural network,” Expert Systems with Applications , vol. 237, p. 121718, 2024

    work page 2024

  6. [6]

    Gtr-ga: Harnessing the power of graph-based neural networks and genetic algorithms for text augmentation,

    A. Onan, “Gtr-ga: Harnessing the power of graph-based neural networks and genetic algorithms for text augmentation,” Expert systems with applications, vol. 232, p. 120908, 2023

    work page 2023

  7. [7]

    Unsupervised euclidean distance attack on network embedding,

    S. Yu, J. Zheng, J. Chen, Q. Xuan, and Q. Zhang, “Unsupervised euclidean distance attack on network embedding,” in 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). IEEE, 2020, pp. 71–77

    work page 2020

  8. [8]

    An improved differential evolution framework using network topology information for critical nodes detection,

    S. Yu, Y . Wang, J. Li, X. Fang, J. Chen, Z. Zheng, and C. Fu, “An improved differential evolution framework using network topology information for critical nodes detection,” IEEE Transactions on Compu- tational Social Systems , vol. 10, no. 2, pp. 448–457, 2022

    work page 2022

Show all 54 references
  1. [9]

    Target defense against link-prediction-based attacks via evolutionary perturbations,

    S. Yu, M. Zhao, C. Fu, J. Zheng, H. Huang, X. Shu, Q. Xuan, and G. Chen, “Target defense against link-prediction-based attacks via evolutionary perturbations,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 2, pp. 754–767, 2019

    2019

  2. [10]

    Influence maximization in complex networks through optimal percolation,

    F. Morone and H. A. Makse, “Influence maximization in complex networks through optimal percolation,” Nature, vol. 524, no. 7563, pp. 65–68, 2015

    2015

  3. [11]

    Framework of evolutionary algorithm for investigation of influential nodes in complex networks,

    Y . Liu, X. Wang, and J. Kurths, “Framework of evolutionary algorithm for investigation of influential nodes in complex networks,” IEEE Transactions on Evolutionary Computation , vol. 23, no. 6, pp. 1049– 1063, 2019

    2019

  4. [12]

    Ga- based multipopulation synergistic gene screening strategy on critical nodes detection,

    S. Yu, J. Li, X. Fang, Y . Wang, J. Wang, Q. Xuan, and C. Fu, “Ga- based multipopulation synergistic gene screening strategy on critical nodes detection,” IEEE Transactions on Computational Social Systems , 2023

    2023

  5. [13]

    An interactive co- evolutionary framework for multi-objective critical node detection on large-scale complex networks,

    L. Zhang, H. Zhang, H. Yang, Z. Liu, and F. Cheng, “An interactive co- evolutionary framework for multi-objective critical node detection on large-scale complex networks,” IEEE Transactions on Network Science and Engineering, vol. 10, no. 3, pp. 1722–1735, 2023

    2023

  6. [14]

    Multi-objective opti- mization of critical node detection based on cascade model in complex networks,

    L. Zhang, J. Xia, F. Cheng, J. Qiu, and X. Zhang, “Multi-objective opti- mization of critical node detection based on cascade model in complex networks,” IEEE Transactions on Network Science and Engineering , vol. 7, no. 3, pp. 2052–2066, 2020

    2020

  7. [15]

    Vital nodes identification via evolutionary algorithm with percolation optimization in complex networks,

    Y . Liu, Y . Zhong, X. Li, P. Zhu, and Z. Wang, “Vital nodes identification via evolutionary algorithm with percolation optimization in complex networks,” IEEE Transactions on Network Science and Engineering , 2024

    2024

  8. [16]

    Ga-based q-attack on community detection,

    J. Chen, L. Chen, Y . Chen, M. Zhao, S. Yu, Q. Xuan, and X. Yang, “Ga-based q-attack on community detection,” IEEE Transactions on Computational Social Systems , vol. 6, no. 3, pp. 491–503, 2019

    2019

  9. [17]

    Hiding ourselves from community detection through genetic algorithms,

    D. Liu, Z. Chang, G. Yang, and E. Chen, “Hiding ourselves from community detection through genetic algorithms,” Information Sciences, vol. 614, pp. 123–137, 2022

    2022

  10. [18]

    Multiscale evolu- tionary perturbation attack on community detection,

    J. Chen, Y . Chen, L. Chen, M. Zhao, and Q. Xuan, “Multiscale evolu- tionary perturbation attack on community detection,” IEEE Transactions on Computational Social Systems , vol. 8, no. 1, pp. 62–75, 2020

    2020

  11. [19]

    A self-adaptive evolution- ary deception framework for community structure,

    J. Zhao, Z. Wang, J. Cao, and K. H. Cheong, “A self-adaptive evolution- ary deception framework for community structure,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 53, no. 8, pp. 4954– 4967, 2023

    2023

  12. [20]

    Higher order connection enhanced community detection in adversarial multiview networks,

    L. Huang, C.-D. Wang, and S. Y . Philip, “Higher order connection enhanced community detection in adversarial multiview networks,” IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 3060–3074, 2021

    2021

  13. [21]

    Gani: Global attacks on graph neural networks via imperceptible node injections,

    J. Fang, H. Wen, J. Wu, Q. Xuan, Z. Zheng, and K. T. Chi, “Gani: Global attacks on graph neural networks via imperceptible node injections,” IEEE Transactions on Computational Social Systems , 2024

    2024

  14. [22]

    Ad- versarial attack on graph structured data,

    H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, and L. Song, “Ad- versarial attack on graph structured data,” in International conference on machine learning . PMLR, 2018, pp. 1115–1124

    2018

  15. [23]

    Performance analysis and cpu vs gpu compari- son for deep learning,

    E. Buber and D. Banu, “Performance analysis and cpu vs gpu compari- son for deep learning,” in 2018 6th International Conference on Control Engineering & Information Technology (CEIT) . IEEE, 2018, pp. 1–6

    2018

  16. [24]

    An efficient fine- grained parallel genetic algorithm based on gpu-accelerated,

    J.-M. Li, X.-J. Wang, R.-S. He, and Z.-X. Chi, “An efficient fine- grained parallel genetic algorithm based on gpu-accelerated,” in 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007, pp. 855–862

    2007

  17. [25]

    Parallel genetic algorithms with gpu comput- ing,

    J. R. Cheng and M. Gen, “Parallel genetic algorithms with gpu comput- ing,” in Industry 4.0-Impact on Intelligent Logistics and Manufacturing . IntechOpen, 2020

    2020

  18. [26]

    Evojax: Hardware-accelerated neuroevo- lution,

    Y . Tang, Y . Tian, and D. Ha, “Evojax: Hardware-accelerated neuroevo- lution,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 308–311

    2022

  19. [27]

    evosax: Jax-based evolution strategies,

    R. T. Lange, “evosax: Jax-based evolution strategies,” arXiv preprint arXiv:2212.04180, 2022

    2022 arXiv

  20. [28]

    Evotorch: scalable evolutionary computation in python,

    N. E. Toklu, T. Atkinson, V . Micka, P. Liskowski, and R. K. Srivastava, “Evotorch: scalable evolutionary computation in python,” arXiv preprint arXiv:2302.12600, 2023. 12

    2023 arXiv

  21. [29]

    Evox: A dis- tributed gpu-accelerated framework for scalable evolutionary computa- tion,

    B. Huang, R. Cheng, Z. Li, Y . Jin, and K. C. Tan, “Evox: A dis- tributed gpu-accelerated framework for scalable evolutionary computa- tion,” IEEE Transactions on Evolutionary Computation , 2024

    2024

  22. [30]

    Networks formed from interdependent networks,

    J. Gao, S. V . Buldyrev, H. E. Stanley, and S. Havlin, “Networks formed from interdependent networks,” Nature physics, vol. 8, no. 1, pp. 40–48, 2012

    2012

  23. [31]

    The critical node detection problem in networks: A survey,

    M. Lalou, M. A. Tahraoui, and H. Kheddouci, “The critical node detection problem in networks: A survey,” Computer Science Review , vol. 28, no. MAY , pp. 92–117, 2018

    2018

  24. [32]

    Inferring the origin of an epidemic with a dynamic message-passing algorithm,

    A. Y . Lokhov, M. M ´ezard, H. Ohta, and L. Zdeborov ´a, “Inferring the origin of an epidemic with a dynamic message-passing algorithm,” Physical Review E , vol. 90, no. 1, p. 012801, 2014

    2014

  25. [33]

    Serial and parallel genetic algorithms as function optimizers,

    V . S. Gordon and L. D. Whitley, “Serial and parallel genetic algorithms as function optimizers,” in Proceedings of the 5th International Con- ference on Genetic Algorithms . San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1993, p. 177–183

    1993

  26. [34]

    Parallel genetic algorithms: A survey,

    A. J. Chipperfield and P. Fleming, “Parallel genetic algorithms: A survey,” 1994

    1994

  27. [35]

    A parallel global multiobjective framework for optimization: pagmo,

    F. Biscani and D. Izzo, “A parallel global multiobjective framework for optimization: pagmo,” Journal of Open Source Software , vol. 5, no. 53, p. 2338, 2020

    2020

  28. [36]

    Accelerating genetic algorithms with gpu computing: A selective overview,

    J. R. Cheng and M. Gen, “Accelerating genetic algorithms with gpu computing: A selective overview,” Computers & Industrial Engineering, vol. 128, pp. 514–525, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S036083521830665X

    2019

  29. [37]

    An experimental study of the small world problem,

    J. Travers and S. Milgram, “An experimental study of the small world problem,” in Social networks. Elsevier, 1977, pp. 179–197

    1977

  30. [38]

    An information flow model for conflict and fission in small groups,

    W. W. Zachary, “An information flow model for conflict and fission in small groups,” Journal of anthropological research , vol. 33, no. 4, pp. 452–473, 1977

    1977

  31. [39]

    The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations: can geographic isolation explain this unique trait?

    D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, “The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations: can geographic isolation explain this unique trait?” Behavioral Ecology and Sociobiol...

    2003

  32. [40]

    Community structure in social and biological networks,

    M. Girvan and M. E. Newman, “Community structure in social and biological networks,” Proceedings of the national academy of sciences , vol. 99, no. 12, pp. 7821–7826, 2002

    2002

  33. [41]

    Graph evolution: Den- sification and shrinking diameters,

    J. Leskovec, J. Kleinberg, and C. Faloutsos, “Graph evolution: Den- sification and shrinking diameters,” ACM transactions on Knowledge Discovery from Data (TKDD) , vol. 1, no. 1, pp. 2–es, 2007

    2007

  34. [42]

    Self-organized critical forest-fire model,

    B. Drossel and F. Schwabl, “Self-organized critical forest-fire model,” Physical review letters, vol. 69, no. 11, p. 1629, 1992

    1992

  35. [43]

    On the evolution of random graphs,

    P. Erdos, A. R ´enyi et al. , “On the evolution of random graphs,” Publ. math. inst. hung. acad. sci , vol. 5, no. 1, pp. 17–60, 1960

    1960

  36. [44]

    Emergence of scaling in random net- works,

    A.-L. Barab ´asi and R. Albert, “Emergence of scaling in random net- works,” science, vol. 286, no. 5439, pp. 509–512, 1999

    1999

  37. [45]

    Variable population memetic search: A case study on the critical node problem,

    Y . Zhou, J.-K. Hao, Z.-H. Fu, Z. Wang, and X. Lai, “Variable population memetic search: A case study on the critical node problem,” IEEE Transactions on Evolutionary Computation, vol. 25, no. 1, pp. 187–200, 2020

    2020

  38. [46]

    A critical look at the evaluation of gnns under heterophily: Are we really making progress?

    O. Platonov, D. Kuznedelev, M. Diskin, A. Babenko, and L. Prokhorenkova, “A critical look at the evaluation of gnns under heterophily: Are we really making progress?” arXiv preprint arXiv:2302.11640, 2023

    2023 arXiv

  39. [47]

    Automating the construction of internet portals with machine learning,

    A. K. McCallum, K. Nigam, J. Rennie, and K. Seymore, “Automating the construction of internet portals with machine learning,” Information Retrieval, vol. 3, pp. 127–163, 2000

    2000

  40. [48]

    Collective classification in network data,

    P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi- Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, pp. 93–93, 2008

    2008

  41. [49]

    Community structure in jazz,

    P. M. Gleiser and L. Danon, “Community structure in jazz,” Advances in complex systems , vol. 6, no. 04, pp. 565–573, 2003

    2003

  42. [50]

    A novel community detection based genetic algorithm for feature selection,

    M. Rostami, K. Berahmand, and S. Forouzandeh, “A novel community detection based genetic algorithm for feature selection,” Journal of Big Data, vol. 8, no. 1, p. 2, 2021

    2021

  43. [51]

    A general evolutionary framework for different classes of critical node problems,

    R. Aringhieri, A. Grosso, P. Hosteins, and R. Scatamacchia, “A general evolutionary framework for different classes of critical node problems,” Engineering Applications of Artificial Intelligence, vol. 55, pp. 128–145, 2016

    2016

  44. [52]

    Memetic search for identifying critical nodes in sparse graphs,

    Y . Zhou, J.-K. Hao, and F. Glover, “Memetic search for identifying critical nodes in sparse graphs,” IEEE transactions on cybernetics , vol. 49, no. 10, pp. 3699–3712, 2018

    2018

  45. [53]

    Architecture- based performance evaluation of genetic algorithms on multi/many-core systems,

    L. Zheng, Y . Lu, M. Ding, Y . Shen, M. Guoz, and S. Guo, “Architecture- based performance evaluation of genetic algorithms on multi/many-core systems,” in 2011 14th IEEE International Conference on Computational Science and Engineering . IEEE, 2011, pp. 321–334

    2011

  46. [54]

    Cheng, M

    J. Cheng, M. Grossman, and T. McKercher, Professional CUDA c programming. John Wiley & Sons, 2014

    2014

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.