IJIRCSTJournal International Journal of Innovative Research in Computer Science and Technology I S Volume 13 Issue 3 Computer Science English May - June 2025 N N Y 2025 06 17 Computer Sciences Autoencoder-Based Adaptive Multi-Objective Particle Swarm Optimization for Gene Selection English Y 188 196 Sumet Mehta English Y Fei Han English N Muhammad Sohail English N Arfan Nagra English N Qinghua Ling English N https://doi.org/10.55524/ijircst.2025.13.3.26 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. English Microarray gene selection, multi-objective optimization, particle swarm optimization, autoencoder. https://ijircst.org/abstract.php?article_id=1389