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Memetic differential evolution with niching strategy for efficient CNN architecture search
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DOI：10.1007/s12293-026-00522-5.png)
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Abstract
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Deep convolutional neural networks (CNNs) are highly effective for a wide range of computer vision tasks, especially image classification. However, creating an optimal CNN architecture is a complex, labor-intensive process that demands substantial manual effort and expert domain knowledge. Neural Architecture Search (NAS) automates this task, with evolutionary algorithms (EAs) offering strong potential but often struggling in multimodal search spaces. Neural Architecture Search (NAS) aims to automate this process, with evolutionary algorithms (EAs) showing strong potential; however, conventional EAs often struggle in multimodal search spaces due to limited exploration–exploitation balance. The paper introduces a new memetic differential evolution (DE) algorithm with a niching strategy and a Gaussian distribution-based local search to effectively explore and exploit the CNN architectural search space for image classification tasks, named MDE/NS. The proposed method is evaluated on standard image benchmarks, including MNIST and its variants, Fashion-MNIST, and SVHN, and compared with competitive CNN models, with additional validation on NAS-Bench-101 and NAS-Bench-201 confirming its effectiveness under standardized NAS benchmarks. A case study on COVID-19 chest X-ray images further demonstrates the adaptability of the evolved architectures to real-world classification tasks.
Keywords:
Convolutional Neural Network (CNN)
Memetic Algorithm (MA)
Niching Strategy
Image Classification
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