Advancing Hyperparameter Optimization in Deep Neural Networks: A Genetic Algorithm Approach

Abstract

Deep neural networks (DNNs) have shown outstanding performance in image recognition, natural language processing, and time-series prediction. However, they are very much at the mercy of the hyperparameters, which in turn makes manual tuning a very labor-intensive and computationally expensive task. In this study, we examine the use of Genetic Algorithms (GAs), which are a type of evolutionary metaheuristic, for DNNs hyperparameter optimization. We systemically encode and evolve candidate solutions, which in turn allows for the efficient traversal of large-scale complex hyperparameter spaces. We present a detailed review of recent research, propose a GA-based optimization framework, and report on the empirical improvements we observed in many deep learning tasks. In addition, we see that our proposed approach does in fact improve on accuracy, computational efficiency, and adaptability when compared to traditional tuning methods. We also consider practical applications, including image classification, time-series forecasting, and disaster risk assessment. This study further analyzes the advantages, limitations, and prospective future developments of GA-driven DNN optimization.

Introduction

Recent advances in deep neural networks (DNNs) have revolutionized fields like computer vision and natural language processing, but optimizing their hyperparameters remains challenging due to model complexity and manual tuning inefficiency. Genetic Algorithms (GAs), inspired by natural evolution, have emerged as a powerful metaheuristic approach for automated hyperparameter optimization. GAs effectively navigate large, non-convex search spaces, avoid local minima, and are well-suited for parallel processing.

Traditional methods such as manual search, grid, random, and Bayesian optimization have limitations, especially as search spaces grow. Metaheuristics like particle swarm optimization and ant colony optimization perform well, but GAs generally strike a better balance between accuracy and efficiency. Advances like variable-length chromosome encoding and multi-objective GAs (e.g., NSGA-II/III) allow simultaneous optimization of accuracy, model complexity, and computational costs. Hybrid methods combining GAs with other optimizers improve convergence and avoid local optima.

The proposed GA framework encodes hyperparameters as chromosomes, evolves populations through selection, crossover, and mutation, and evaluates fitness mainly via validation accuracy. Multi-objective criteria and termination conditions enhance flexibility. Implementation uses popular ML libraries with parallelized evaluation to handle computational demands.

Experiments demonstrate that GA-optimized DNNs outperform manual or random search on diverse tasks such as image classification (MNIST), time-series forecasting, environmental risk modeling, and medical diagnosis. Multi-objective and hybrid GA variants yield better accuracy and efficiency, with applications spanning IoT, edge AI, industrial optimization, and more.

Advantages: Global search ability, adaptability, multi-objective optimization, and ease of parallelism.

Limitations: High computational cost, risk of premature convergence, sensitivity to initial population, and stochastic variability.

Future directions: Hybrid metaheuristics, distributed and dynamic GAs, multi-fidelity evaluations, integration of domain knowledge, and enhancing model interpretability.

Conclusion

Genetic algorithms provide a resilient, scalable, and flexible approach to hyperparameter optimization for deep neural networks. 5 Conclusion In summary, by effectively traversing challenging search spaces, enabling flexible model architectures, and coordinating multiple targets, we demonstrated that GA-based methods consistently improve the performance, efficiency, and universality of DNN in a diverse range of domains. Further studies on hybridization, distributed optimization, and integration with other metaheuristics are expected to cement GAs as a foundation for the development of next-generation deep learning models.

References

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Copyright

Copyright © 2025 Mr. V. Satish, Pilli Harshavardhini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.