Long-tailed image classification faces challenges of data imbalance and poor feature representation for tail classes, leading to biased predictions favoring head classes. While most existing methods rely on deep neural networks (DNNs), they typically require large amounts of training data and lack interpretability. Genetic Programming (GP) has shown promise in few-shot learning but has seldom been investigated in long-tailed image classification, primarily due to its limited ability to handle class imbalance and its tendency for fitness functions to be biased toward head classes. To fill this gap, this paper proposes a multi-expert GP method for long-tailed image classification.We develop three objective functions, each serving as an expert: 1) a longtailed expert focusing on head-class performance; 2) a balanced-class expert that promotes equal class representation; and 3) an inverse long-tailed expert emphasizing tail classes. This triexpert framework enables GP to jointly optimize complementary objectives and learn robust feature representations for both head and tail classes. To further improve classification performance, the evolved GP individuals from the final population are used to train base learners, and their outputs are integrated via a voting-based ensemble model. Experimental results demonstrate that the proposed method outperforms state-of-the-art GP and DNN approaches without pretraining across seven long-tailed image classification datasets.
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