GEGO: A Hybrid Golden Eagle and Genetic Optimization Algorithm for Efficient Hyperparameter Tuning in Resource-Constrained Environments

TL;DR

GEGO is a novel hybrid metaheuristic combining Golden Eagle Optimization and genetic operators, enhancing hyperparameter tuning efficiency and robustness in resource-limited neural network training scenarios.

Contribution

The paper introduces GEGO, embedding genetic operators into GEO's iterative process, improving diversity and convergence for hyperparameter optimization.

Findings

GEGO outperforms GEO and GA on benchmark functions.

GEGO achieves higher accuracy on MNIST.

GEGO demonstrates stable convergence and robustness.

Abstract

Hyperparameter tuning is a critical yet computationally expensive step in training neural networks, particularly when the search space is high dimensional and nonconvex. Metaheuristic optimization algorithms are often used for this purpose due to their derivative free nature and robustness against local optima. In this work, we propose Golden Eagle Genetic Optimization (GEGO), a hybrid metaheuristic that integrates the population movement strategy of Golden Eagle Optimization with the genetic operators of selection, crossover, and mutation. The main novelty of GEGO lies in embedding genetic operators directly into the iterative search process of GEO, rather than applying them as a separate evolutionary stage. This design improves population diversity during search and reduces premature convergence while preserving the exploration behavior of GEO. GEGO is evaluated on standard unimodal, multimodal, and composite benchmark functions from the CEC2017 suite, where it consistently outperforms its constituent algorithms and several classical metaheuristics in terms of solution quality and robustness. The algorithm is further applied to hyperparameter tuning of artificial neural networks on the MNIST dataset, where GEGO achieves improved classification accuracy and more stable convergence compared to GEO and GA. These results indicate that GEGO provides a balanced exploration-exploitation tradeoff and is well suited for hyperparameter optimization under constrained computational settings.