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.
