Autoencoder-Based Adaptive Multi-Objective Particle Swarm Optimization for Gene Selection

Sumet Mehta; Fei Han; Muhammad Sohail; Arfan Nagra; Qinghua Ling

doi:10.55524/ijircst.2025.13.3.26

Abstract

In gene expression analysis, selecting informative genes is essential for uncovering biological mechanisms and identifying potential biomarkers. However, conventional gene selection methods often struggle with scalability and parameter tuning, limiting their effectiveness in large-scale datasets and algorithmic optimization. To overcome these challenges, we propose Autoencoder-based Adaptive Multi-Objective Particle Swarm Optimization for Gene Selection (AAMOPSO). Our approach incorporates an autoencoder-based preprocessing step to enhance scalability by learning a compressed representation of gene expression data, reducing dimensionality while retaining critical features. Additionally, we introduce an Adaptive Parameter Tuning mechanism within the Multi-Objective Particle Swarm Optimization (MOPSO) framework, dynamically adjusting algorithm parameters based on real-time performance metrics. Extensive experiments on four benchmark microarray datasets demonstrate that AAMOPSO consistently outperforms existing state-of-the-art methods in classification accuracy and the compactness of selected gene subsets.

Keywords

Microarray gene selection, multi-objective optimization, particle swarm optimization, autoencoder.

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Cites this article as

S. Mehta, F. Han, M. Sohail, A. Nagra, Q. Ling, "Autoencoder-Based Adaptive Multi-Objective Particle Swarm Optimization for Gene Selection", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-13, Issue-3, Page No-188-196, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.3.26

Corresponding Author

Sumet Mehta

School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

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