GeneticNAS: a novel self-evolving neural architecture for advanced ASD screening

The early identification of Autism Spectrum Disorder (ASD) remains a critical challenge in neurodevelopmental research, with current diagnostic processes often delayed by subjective assessments and limited clinical resources. This paper presents a memory-efficient Neural Architecture Search framework that autonomously identifies optimum neural network structures for ASD classification. Unlike existing genetic algorithm-based NAS approaches requiring over 16GB GPU memory, our framework achieves 76% memory reduction while maintaining superior performance. Our approach presents three key innovations: (1) a novel search space integrating simple, residual, and bottleneck operations with [Formula: see text] complexity for L layers; (2) a memory-efficient genetic algorithm that decreases GPU memory consumption by [Formula: see text] relative to current methodologies while preserving search efficacy; and (3) an adaptive fitness function that equilibrates model performance with computational complexity. Through comprehensive experiments utilizing a substantial dataset ([Formula: see text]; [Formula: see text], [Formula: see text]), our methodology attained a classification accuracy of [Formula: see text] ([Formula: see text] CI: 94.89-[Formula: see text]) and area under the Receiver Operating Characteristic (ROC) curve of 0.986, which markedly surpassed existing state-of-the-art techniques (traditional CNN: 92.3%, ResNet-based: 94.1%, LSTM: 93.7%). The framework achieves this performance with 2.8M parameters and 15ms processing time per sample, demonstrating practical viability for clinical deployment in resource-constrained settings where current diagnostic procedures extend 4-5 years after symptom onset.