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Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional machine learning techniques in solving various real-life problems in computational intelligence fields, such as image classification. However, most existing CNN architectures were handcrafted from scratch and required significant amounts of problem domain knowledge from designers. A novel deep learning method abbreviated as TLBOCNN is proposed in this paper by leveraging the excellent global search ability of teaching–learning-based optimization (TLBO) to obtain an optimal design of network architecture for a CNN based on the given dataset with symmetrical distribution of each class of data samples. A variable-length encoding scheme is first introduced in TLBOCNN to represent each learner as a potential CNN architecture with different layer parameters. During the teacher phase, a new mainstream architecture computation scheme is designed to compute the mean parameter values of CNN architectures by considering the information encoded into the existing population members with variable lengths. The new mechanisms of determining the differences between two learners with variable lengths and updating their positions are also devised in both the teacher and learner phases to obtain new learners. Extensive simulation studies report that the proposed TLBOCNN achieves symmetrical performance in classifying the majority of MNIST-variant datasets, displays the highest accuracy, and produces CNN models with the lowest complexity levels compared to other state-of-the-art methods due to its promising search ability.