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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Hybrid Algorithms Based on Two Evolutionary Computations for Image Classification.

Peiyang Wei^1,2,3,4,5,6, Rundong Zou², Jianhong Gan^2,4,6

¹School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Biomimetics (Basel, Switzerland)

|August 27, 2025

Summary

This summary is machine-generated.

This study introduces a hybrid optimization algorithm (HGAO) to enhance DenseNet-121 for image classification. The HGAO algorithm effectively optimizes hyperparameters, improving both classification accuracy and model stability.

Keywords:

DenseNet-121 giant armadillo optimization algorithm horned lizard optimization algorithm hyperparameter optimization image classification

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Convolutional neural networks (CNNs), including advanced models like DenseNet-121, excel in image classification but face challenges in hyperparameter optimization and gradient stability.
Evolutionary algorithms offer potential solutions due to their exploration and exploitation strengths, addressing limitations in current CNN models.

Purpose of the Study:

To improve image classification performance by optimizing DenseNet-121 hyperparameters using a novel hybrid evolutionary algorithm.
To enhance classification accuracy and model stability, while mitigating issues like gradient vanishing and exploding.

Main Methods:

A hybrid algorithm (HGAO) combining the horned lizard algorithm with quadratic interpolation and giant armadillo optimization with Newton interpolation was developed.
The HGAO algorithm was employed to optimize key hyperparameters, specifically learning rate and dropout rate, for the DenseNet-121 model.
The optimized DenseNet-121 model was evaluated on five diverse image datasets, with performance compared against nine state-of-the-art algorithms using accuracy, precision, recall, and F1-score metrics.

Main Results:

Hyperparameter optimization using HGAO resulted in more effective parameter combinations, leading to significant performance improvements.
On the training set, accuracy increased by up to 0.5% and loss decreased by 0.018.
On the test set, accuracy improved by 0.5% and loss decreased by 54 points, demonstrating enhanced classification performance and stability.

Conclusions:

The HGAO algorithm provides an effective method for optimizing DenseNet-121 hyperparameters, boosting classification accuracy and model stability.
The proposed approach successfully addresses gradient difficulties and enhances the overall effectiveness of hyperparameter optimization for deep learning models in image classification.