reviews: 
Comments: This paper introduces a novel hyperparameter scheme based on genetic algorithms and Bayesian optimization. The proposed approach addresses the distribution shift induced by the nature of time series with a sliding window approach.  The approach is applied to time-series prediction on multiple datasets and various models.
Strengths: - the paper is clear and concise, though it presents some nits reported below
 - the method is applied on multiple datasets and on a variety of different models.
Weaknesses: Important weaknesses:
 - the contributions are strictly practical, thus it is of fundamental importance that the method is accessible to researchers and reproducible. Particularly, ICRL strongly encourage to add a “Reproducibility Statement” (https://iclr.cc/Conferences/2025/AuthorGuide), and the paper is missing it. Furthermore, no code is provided as supplementary material, making the results hard to reproduce and inaccessible
 - table 1 shows that it’s the TPE addition that outperforms the other method, but no experiment shows that the proposed method is better than just TPE.
 - results are not reported with confidence intervals. Thus, there is no way to check if the results are just due to variance.
 - No information is given on the training procedure of the GA counterpart. Furthermore, multiple GA algorithms are present in the literature, but it’s never reported which is the one that is applied for the results.
Minor weaknesses
 - line 178, TPE is criticized for its ability to scale badly with dimensionality, though the paper uses it to initialize the population, inheriting its downside
 - the 4th contribution point is pointless, given that no code is provided as supplementary material
 - the 2nd contribution point states that the proposed approach should address the distributions shift induced by the nature of time-series. However, nowhere in the paper this is investigated, and specifically, nowhere is addressed what are the other options apart from SWGA and why they should fall short in time series predictions
 - in the “conclusion” section, it is stated “Additionally, we also demonstrate the good scalability of SWGA”. However, no reference to other algorithms/approaches is given. Thus, with a lack of baselines, it’s completely irrelevant

My recommendation is to reject the paper.
The main reasons behind this opinion are:
 - the contributions are minors, and they can be summarized in a smart initialization of the GA using TPE, and a sliding window training approach.
 - the lack of reproducibility of the results
 - the narrow comparison with other approaches.
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Writing concerns:
 - citations should be between parenthesis if not part of the main text (e.g. line 39-40 and then the rest of the paper, respectively use \citep for parenthesis and cite for normal citation)
 - line 68, TPE was never defined (will be defined later, though it should be defined on the first usage)
 - “But, their full” line 107
 - “K-fold cross-validation effectively reduces the risk of overfitting”: how? It’s a validation method, not a regularization
 - The equation on line 140 is not numbered, and it contains 2 “i” indexes. Please use different letters to avoid confusion
 - line 161 “while domain adaptation is to”
 - line 258 263, the pseudocode contains a line break that is irrelevant
Personal opinions:
 - contribution 2 and 3 are not contributions, but positive aspects of the method, maybe better to write them outside of the bullet points
 - the first two paragraphs in 4.1 are repetitive, and the explanation on how the GA works should be part of the background section
Recommendation: 1
Reviewer confidence: 3
Soundness/correctness: 1
Clarity: 2