GENETIC ALGORITHM-BASED HYPERPARAMETER OPTIMIZATION IN DEEP LEARNING FOR HIGH-ACCURACY LOGISTICS EXPENDITURE CLASSIFICATION
DOI:
https://doi.org/10.33480/jitk.v12i1.7840Keywords:
Artificial Neural Network, Classification, Convolutional Neural Network, Genetic Algorithm, Logistics EfficiencyAbstract
Traditional reactive logistics auditing often fails to detect hidden operational inefficiencies, particularly in transaction data with imbalanced class distributions. This study aims to analyze the effect of Genetic Algorithm (GA)-based hyperparameter optimization on the performance of deep learning models for logistics expenditure efficiency classification and to compare its performance with baseline Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) models. This study used a real-world logistics transaction dataset, with the Cost per Kilogram metric employed as the basis for classification labeling. The research stages included data collection, preprocessing and feature engineering, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), development of ANN and CNN models, ANN hyperparameter optimization using GA, and comparative evaluation based on accuracy, precision, and recall. The results showed that the baseline ANN and CNN models obtained a recall of 0.00 in detecting inefficient transactions, whereas the GA-optimized ANN achieved an accuracy of 91% and a perfect recall of 1.00. These findings indicate that GA-based hyperparameter optimization improves the model's ability to detect inefficient transactions and reduces diagnostic blind spots in imbalanced logistics financial data. This study contributes a high-accuracy classification approach that can support proactive financial monitoring and automated auditing in the logistics sector.
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[1] M. N. Trisolvena, F. Y. Wattimena, and P. P. Untajana, “Logistics Efficiency in Product Distribution with Genetic Algorithms for Optimal Routes,” Int. J. Softw. Eng. Comput. Sci., vol. 4, no. 1, pp. 247–262, 2024, doi: 10.35870/ijsecs.v4i1.2045.
[2] T. Tang, J. Yao, Y. Wang, Q. Sha, and H. Feng, “Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions,” arXiv preprint arXiv:2504.15491, Apr. 2025, doi: https://arxiv.org/abs/2504.15491.
[3] D. W. Naaman, B. T. Ahmed, and I. M. Ibrahim, “Optimization by Nature: A Review of Genetic Algorithm Techniques,” The Indonesian Journal of Computer Science, vol. 14, no. 1, pp. 268–284, Feb. 2025, doi: 10.33022/ijcs.v14i1.4596.
[4] M. V. T. K. Trinh, “RESEARCH ON GENETIC ALGORITHMS: CONCEPTS, MODELS, AND APPLICATIONS,” Int. J. Educ. Soc. Sci. Res., vol. 8, no. 3, pp. 354–373, 2025, doi: 10.2139/ssrn.5260361.
[5] A. G. Ham, C. Nafornita, V. C. Vesa, G. Copacean, V. D. Davidovici, and I. Nafornita, “Grid Search and Genetic Algorithm Optimization of Neural Networks for Automotive Radar Object Classification,” Sensors, vol. 25, no. 19, p. 6017, Sep. 2025, doi: 10.3390/s25196017.
[6] L. Xin, P. Xu, and G. Manyi, “Logistics Distribution Route Optimization Based on Genetic Algorithm,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/8468438.
[7] E. Boz, “Optimizing smart warehousing with Genetic Algorithm and Deep Q-Learning,” RAIRO - Operations Research, vol. 59, no. 5, pp. 3285–3307, Oct. 2025, doi: 10.1051/ro/2025132.
[8] A. Mehdary, A. Chehri, A. Jakimi, and R. Saadane, “Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection,” Sensors, vol. 24, no. 4, p. 1230, Feb. 2024, doi: 10.3390/s24041230.
[9] F. Zahrae and E. M. Amri, “A New Optimization Model for MLP Hyperparameter Tuning : Modeling and Resolution by Real-Coded Genetic Algorithm,” Neural Process. Lett., vol. 56, no. 2, pp. 1–31, 2024, doi: 10.1007/s11063-024-11578-0.
[10] V. Satish and P. Harshavardhini, “Advancing Hyperparameter Optimization in Deep Neural Networks: A Genetic Algorithm Approach,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 13, no. 7, pp. 2254–2257, Jul. 2025, doi: 10.22214/ijraset.2025.73376.
[11] M. Binjubier, M. A. Ismail, M. Othman, S. Kasim, and H. Amnur, “Optimizing Genetic Algorithm by Implementation of An Enhanced Selection Operator,” Int. J. Informatics Vis., vol. 8, no. 3–2, pp. 1643–1650, 2024, doi: 10.62527/joiv.8.3-2.3449.
[12] X. Xiao, M. Yan, S. Basodi, C. Ji, and Y. Pan, “Efficient hyperparameter optimization in deep learning using a variable length genetic algorithm,” arXiv preprint arXiv:2006.12703, Jun. 2020, doi: 10.48550/arXiv.2006.12703.
[13] C. Kim and I. Joe, “A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization,” IEEE Access, vol. 12, pp. 29590–29601, 2024, doi: 10.1109/ACCESS.2024.3508269.
[14] B. P. Kaur et al., “A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI,” PLoS One, vol. 19, no. 12, Dec. 2024, doi: 10.1371/journal.pone.0308015.
[15] N. Alkhafaji and T. V. A. Al-sherbaz, “Integrated Genetic Algorithm and Deep Learning Approach for Effective Cyber-Attack Detection and Classification in Industrial Internet of Things ( IIoT ) Environments,” Arab. J. Sci. Eng., vol. 50, no. 15, pp. 12071–12095, 2025, doi: 10.1007/s13369-024-09663-6.
[16] M. Nehzati, “Memories - Materials , Devices , Circuits and Systems Optimization of deep learning algorithms for large digital data processing using evolutionary neural networks,” Memories - Mater. Devices, Circuits Syst., vol. 9, p. 100126, 2025, doi: 10.1016/j.memori.2025.100126.
[17] Y. Yuan, W. Wang, G. M. Coghill, and W. Pang, “A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks,” arXiv preprint arXiv:2101.09300, Jan. 2021, doi: 10.48550/arXiv.2101.09300.
[18] B. Brzęk, B. Probierz, and J. Kozak, “Exploration-Driven Genetic Algorithms for Hyperparameter Optimisation in Deep Reinforcement Learning,” Applied Sciences, vol. 15, no. 4, p. 2067, Feb. 2025, doi: 10.3390/app15042067.
[19] S. Biswas, A. D. Cobb, A. Sistrunk, N. Ramakrishnan, and B. Jalaian, “Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization,” arXiv preprint arXiv:2012.06453, Dec. 2020, doi: 10.48550/arXiv.2012.06453.
[20] H. D. Purnomo, T. Gonsalves, E. Mailoa, F. J. Santoso, and M. R. Pribadi, “Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 3, Art. no. 5730, Jun. 2024, doi: 10.29207/resti.v8i3.5730.
[21] G. Airlangga, “Deep Learning for Anomaly Detection and Fraud Analysis in Blockchain Transactions of the Open Metaverse,” J. Inform. Ekon. Bisnis, vol. 6, pp. 324–329, 2024, doi: 10.37034/infeb.v6i2.865.
[22] F. Hameed and H. Alkhzaimi, “Hybrid genetic algorithm and deep learning techniques for advanced side-channel attacks,” Scientific Reports, vol. 15, no. 1, Art. no. 25728, Jul. 2025, doi: 10.1038/s41598-025-06375-1.
[23] N. M. Kollapally, “Hyperparameter Optimization Using Genetic Algorithm for Extracting Social Determinants of Health Text,” vol. 2, no. Biostec, pp. 300–307, 2024, doi: 10.5220/0012310300003657.
[24] F. Zito, E. Talbi, C. Cavallaro, V. Cutello, and M. Pavone, “Intelligent Systems with Applications Metaheuristics in automated machine learning : Strategies for optimization,” Intell. Syst. with Appl., vol. 26, p. 200532, 2025, doi: 10.1016/j.iswa.2025.200532.
[25] K. R. Ahmed et al., “Deep learning framework for interpretable supply chain forecasting using SOM ANN and SHAP,” Scientific Reports, vol. 15, Art. no. 20452322, Jul. 2025, doi: 10.1038/s41598-025-11510-z.
[26] E. Kurniawan, D. Alia, H. Nurdiansari, and S. P. Wijaya, “Literature Review of Particle Swarm Optimization,” METEOR STIP MARUNDA, vol. 14, no. 2, pp. 100–129, 2024.
[27] J. Yao et al., “Research on hybrid strategy Particle Swarm Optimization algorithm and its applications,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-76010-y.
[28] Q. Li, N. Kamaruddin, J. Zhang, A. S. K. Khoo, and S. Musa, “A novel method of bayesian genetic optimization on automated hyperparameter tuning,” Scientific Reports, vol. 15, Art. no. 6710, 2025, doi: 10.1038/s41598-025-29383-7.
[29] D. Karami, “Supply Chain Network Design Using Particle Swarm Optimization (PSO) Algorithm,” Int. J. Ind. Eng. Oper. Res., vol. 4, no. 1, pp. 1–8, 2022.
[30] S. Bashath et al., “An improved particle swarm optimization based on lévy flight and simulated annealing for high dimensional optimization problem,” Int. J. Adv. Intell. Informatics, vol. 8, no. 1, pp. 115–134, Mar. 2022, doi: 10.26555/ijain.v8i1.818.
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