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REVIEW 4 major objections 4 minor 2 cited by

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

T0 review · 4 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A review chapter argues that the curse of dimensionality is a feature-space failure and that evolutionary algorithms, embedded in feature extraction, are the practical way to escape it.

desk verdict A review chapter that promises a formal definition of the curse of dimensionality and never delivers; the placeholder-laden pseudocode and factual errors make it unsuitable even as an educational resource. read the letter →

arxiv 1908.08006 v1 pith:XTGT4XFS submitted 2019-08-16 cs.NE cs.LGstat.ML

Farid Ghareh Mohammadi , M. Hadi Amini , Hamid R. Arabnia This is my paper · ORCID
classification cs.NEcs.LGstat.ML
keywords evolutionaryalgorithmscurseofdimensionalityfeatureextractionselectionmetaheuristicsdimensionreductiondatascienceswarmintelligence
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

This paper is a tutorial-style argument that the curse of dimensionality, the performance and time-complexity collapse caused by too many features in high-dimensional data, can be attacked inside the preprocessing step by evolutionary algorithms. It defines CoD as a feature-space disease marked by data sparsity, multiple testing, overfitting, and high time complexity, then separates feature extraction into auto-encoder dimension reduction, feature selection, and feature generation. The chapter's practical claim is that nature-inspired metaheuristics such as genetic algorithms, particle swarm optimization, artificial bee colony, ant colony optimization, grey wolf optimizer, and coyote optimization algorithm can find near-optimal feature subsets when classical gradient-based learners would settle in local optima. A sympathetic reader takes away a map of the field and a set of pseudocode templates for embedding these algorithms into a classifier pipeline.

What carries the argument

The central machinery is the generic evolutionary-algorithm loop, presented as a reusable skeleton: represent a candidate feature subset, initialize a population, evaluate a fitness function, select parents, apply crossover and mutation, replace or update the population, and stop on a stall condition. Around this loop the chapter organizes its survey, with the same skeleton appearing through species-specific update rules in genetic algorithms, artificial bee colony, particle swarm optimization, ant colony optimization, grey wolf optimizer, and coyote optimization algorithm, each supplied with pseudocode for feature selection. The other load-bearing object is the three-way taxonomy of feature extraction, namely auto-encoder dimension reduction, feature selection with filter, wrapper, and embedded variants, and feature generation, because it determines where in the pipeline the evolutionary search is inserted.

What would settle it

Re-implement each presented algorithm, especially the ACO and FSA listings whose equations contain placeholder symbols, from the pseudocode alone and run them on a benchmark dataset with a known feature-selection result; if any algorithm cannot be executed or fails to reproduce the cited method's reported behavior, the chapter's claim to provide an accurate overview is falsified.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is organizational and prescriptive: the curse of dimensionality in data science is best understood as a feature-extraction failure, and evolutionary algorithms are the class of optimizers suited to repair it. The chapter maintains that once data is represented by a large set of extracted features, CoD manifests as data sparsity, multiple testing, overfitting, and prohibitive time complexity; classical feature extraction such as PCA is insufficient because it assumes linear correlation and can destroy information on non-linear structures. Feature selection that keeps original feature values is therefore presented as the corrective, and wrapper-based selection in particular is said to outperform filter-based selection at the cost of time. The chapter further claims that evolutionary algorithms are adopted precisely when a problem suffers from placement in local optima rather than global ones, and that their stochastic nature is managed by running them twenty to thirty times and reporting the mean, yielding a stable near-optimal solution.

Load-bearing premise

The whole overview stands or falls on the assumption that the pseudocode and parameter descriptions in Section 4 faithfully represent the cited algorithms, because that is what a reader would use to implement them.

Editorial extensions

If this is right

  • If CoD is a feature-space problem, then preprocessing, not the classifier, is the right place to apply optimization, and embedding evolutionary algorithms into feature extraction should lower time complexity while preserving or improving classifier accuracy.
  • If wrapper-based feature selection consistently outperforms filter-based selection, then accuracy-critical systems should accept the higher runtime and use classifier-driven fitness evaluation, with evolutionary algorithms searching the feature-subset space.
  • If the generic evolutionary-algorithm loop is reliable across species, then a practitioner can port the same representation, selection, crossover, and mutation logic to new problems by swapping the fitness function and the update rule.
  • If evolutionary algorithms are adopted only when local optima are the obstruction, then their value for a given dataset is diagnostic: a problem that is not locally trapping will not benefit from evolutionary search.
  • If the presented algorithms are domain-independent, then the same feature-selection machinery applies across engineering, medicine, network analysis, and image classification without redesigning the optimizer.

Reading between the lines

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

  • The chapter does not provide detailed experimental comparisons among the surveyed algorithms, so a benchmark with identical feature-selection representation would directly test which method is best for CoD.
  • The chapter's emphasis on running each algorithm twenty to thirty times and averaging implies that evolutionary results should be treated as random variables; reporting variance and confidence intervals alongside the mean is a testable extension.
  • The pseudocode contains placeholder symbols and malformed equations in some listings, so a reader who wants a reliable implementation should consult the original papers, and a clean re-derivation of each algorithm from the cited sources is a natural companion.
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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

4 major / 4 minor

Summary. This manuscript is a survey chapter on evolutionary algorithms (EAs) and other metaheuristics for data science, focusing on the curse of dimensionality (CoD), feature extraction and selection, and dimension reduction. The abstract and introduction promise a 'clear and formal definition' of CoD, followed by a survey of feature extraction techniques and an overview of EA families including GA, GP, EP, ABC, PSO, ACO, GWO, COA, CSO, and FSA, with pseudocode for feature selection. The paper also claims to show how nature-inspired algorithms can address CoD in large-scale engineering and science problems, and it references several applications including the authors' own prior work on image steganalysis. The manuscript appears to be an early version of a book chapter, referencing a companion chapter [67] for further details.

Significance. If the survey were accurate and self-contained, it could serve as a useful introductory reference for practitioners applying evolutionary algorithms to feature selection and dimension reduction in high-dimensional data. The authors have assembled a broad bibliography and cover a wide range of algorithms, which is potentially helpful for readers seeking an entry point. However, the paper's central advertised contribution—the formal definition of CoD—is not delivered, and the manuscript contains numerous factual errors, corrupted pseudocode, and incomplete placeholder citations. These issues are not local presentation problems; they undermine the reliability of the survey as a reference. The paper offers no new experimental results or reproducible code, and its educational value is currently limited because readers cannot trust the algorithm descriptions or the historical and technical claims.

major comments (4)
  1. [Section 2.3 / Abstract] The abstract promises 'a clear and formal definition of the CoD problem,' but Section 2.3 provides only informal prose, describing CoD as 'related to the fact that the input data is too huge that no human being can analyze it' and equating it with an 'overabundance of data.' No mathematical definition, complexity statement, or statistical characterization is given. Because the formal definition is the paper's primary advertised contribution, its absence is a load-bearing failure.
  2. [Section 4, Algorithms 2, 4, 6, 8] The pseudocode listings contain corrupted placeholder tokens from a broken font encoding, such as '/v.alt', '/afii10069.ital', and '/y.alt', as well as malformed equations, for example Algorithm 4's input line 'K}≥ 1', Algorithm 6's line 'usin/afii10069.ital Pe = 0.005· N 2c', and Algorithm 8's lines 7 and 17. These listings cannot be executed and are not faithful representations of the cited algorithms, so the overview misleads readers who rely on the pseudocode to understand or implement the methods.
  3. [Section 2.6 / Table 2] The claim that 'Evolutionary algorithms (EAs) is invented not more than 28 years' is historically inaccurate and is contradicted by the paper's own references, which include Fogel's 1965 work on evolutionary programming [39], Koza's 1992 genetic programming book [37], and the historical treatment in [12]. Additionally, Table 2 defines 'SVD' as 'singular value dimension' instead of 'singular value decomposition.' These factual errors reduce the survey's credibility.
  4. [Sections 2.2 and 2.4] The manuscript contains unfinished placeholder citations, notably 'cite all papers from 2.4 here [9, 1]', 'cite all papers from 2.5 here [10, 11]', and 'cite all papers from 2.6 here [12, 13, 14, 15, 13]'. This is direct evidence that the manuscript is not complete and has not been prepared for formal review. A published survey must not include author instructions to itself.
minor comments (4)
  1. [Section 2.3] The statement that CoD is 'due to the large amount of generated/sensed/collected data' is misleading; the curse of dimensionality concerns the difficulties of estimation, sampling, and optimization in high-dimensional spaces, not merely data volume.
  2. [Section 4.2.2] The example expression '4 ∗ tan(x) +/y.alt2' contains an encoding artifact and should be written with proper mathematical notation, e.g., 4*tan(x) + y^2.
  3. [Section 3] The sentence 'PCA completely is used to generate a new dimension using a certain formula' is vague, and the claim that PCA fails for 'circle-based and sine or cosine-based distribution of instances' needs a concrete example or a citation to be informative.
  4. [Section 2.6.1] The text says 'most of research studies are accomplished using SSA' but the intended abbreviation is likely SSGA; SSA is never defined, and the surrounding discussion refers to steady-state genetic algorithms.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the chapter is a survey whose claims are drawn from external sources, and the authors' self-citations are contextual rather than load-bearing.

full rationale

This manuscript is a survey and overview chapter rather than a derivation of new results. It does not fit parameters to data and then rename them as predictions, nor does it define its central term (CoD) in terms of a quantity it later claims to derive. The abstract promises 'a clear and formal definition of the CoD problem,' but Section 2.3 gives only informal prose and external citations; the absence of the promised formalization is a completeness or correctness defect, not a circularity. The self-citations (e.g., [2], [14], [15], [67]) appear in motivational and bibliographic contexts, such as 'Ghareh mohammadi and Arabnia has discussed application of evolutionary algorithms on images... in this study we expanded our investigation' and the conclusion's pointer to '[67]' as related work. These citations do not supply the mathematical content of the survey, and the algorithm descriptions in Section 4 are attributed to external sources such as [13], [41], [48], [56], [59], [60], and [64]. No load-bearing step reduces by construction to a fitted parameter, a self-citation, or a definitional equivalence. Therefore, no circular step can be exhibited, and the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim is expository, so there are no fitted parameters. The chapter relies on domain assumptions about the validity of evolutionary algorithms and the correctness of its algorithm descriptions.

assumptions (3)
  • domain assumption Evolutionary algorithms converge to (near-)optimal solutions over multiple runs.
    Stated repeatedly in Sections 2 and 4 without proof or citation to convergence guarantees.
  • domain assumption Feature selection and dimension reduction preserve classifier performance when done correctly.
    Assumed throughout Section 3 as the motivation for feature extraction.
  • ad hoc to paper The pseudocode listings for each EA are correct representations of the cited algorithms.
    Algorithms 1-8 are presented as implementations of GA, ABC, PSO, ACO, GWO, COA, CSO, and FSA; many contain notation errors and placeholder tokens, so correctness is assumed rather than demonstrated.

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Cite this review

Pith. "Pith review of Evolutionary Computation, Optimization and Learning Algorithms for Data Science." pith.science (2026). https://pith.science/paper/XTGT4XFS

@misc{pith2026190808006,
  author       = {Pith},
  title        = {Pith review of: Evolutionary Computation, Optimization and Learning Algorithms for Data Science},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XTGT4XFS}},
  note         = {Machine review of arXiv:1908.08006}
}
read the original abstract

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms.

Figures

Figures reproduced from arXiv: 1908.08006 by the authors.

Figure 1
Figure 1. Overall structure of this study 8 [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. General process of evolutionary algorithms [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. SSGA (steady state genetic algorithm): Process of updating the population [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: GGA (generational genetic algorithm): The process of generating a new population [PITH_FULL_IMAGE:figures/full_fig_p019_4.png]
Figure 5
Figure 5. Figure 5: Tree presentation of a problem Similar to GA that crossover is conducted on vectors, in GP crossover is done through a tree and only needs to choose two sub-tree. Figure6 expresses that the first two tree has two subset which are selected as a parent. Second tree the b…
Figure 6
Figure 6. Figure 6: Crossover operator in genetic programming [PITH_FULL_IMAGE:figures/full_fig_p021_6.png]

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Forward citations

Cited by 2 Pith papers

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  1. An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises

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    A review of meta-learning algorithms with a taxonomy and a table of prior work; it contributes no new results.

  2. Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

    cs.LG 2019-08 unverdicted

    A review of nature-inspired optimization algorithms applied to feature selection and dimension reduction across image, network, sentiment, and credit-scoring tasks.

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