Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this article develops a divide-and-conquer GP algorithm with knowledge transfer (KT) and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divide-and-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new KT method is proposed to improve GP learning performance. A new fitness function based on log loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-the-art algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and KT in the proposed approach.

中文翻译：

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用于图像分类的具有集成的分而治之的遗传规划算法

遗传编程（GP）已应用于图像分类中的特征学习并取得了可喜的成果。然而，现有基于 GP 的方法的一个主要限制是计算成本高，这可能会限制它们在大规模图像分类任务中的应用。为了解决这个问题，本文开发了一种具有知识转移 (KT) 和集成的分而治之的 GP 算法，以实现图像分类中的快速特征学习。在新的算法框架中，采用分而治之的策略将训练数据和总体分成小的子集或组，以减少计算时间。提出了一种新的 KT 方法来提高 GP 的学习性能。开发了基于对数损失的新适应度函数和新的集成制定策略，以构建有效的图像分类集成。已在 12 个不同难度的图像分类数据集上检查了所提出方法的性能。结果表明，与基于GP的基线算法相比，新方法以更少的计算时间实现了更好的分类性能。与最先进算法的比较表明，新方法在几乎所有比较中都实现了更好或可比的性能。进一步的分析证明了集成公式和 KT 在所提出的方法中的有效性。与最先进算法的比较表明，新方法在几乎所有比较中都实现了更好或可比的性能。进一步的分析证明了集成公式和 KT 在所提出的方法中的有效性。与最先进算法的比较表明，新方法在几乎所有比较中都实现了更好或可比的性能。进一步的分析证明了集成公式和 KT 在所提出的方法中的有效性。