Evolutionary ensemble learning is an emerging research area, and designing an appropriate quality-diversity optimization algorithm to obtain a set of effective and complementary base learners is important. However, how to maintain such a set of learners remains an open issue. This paper proposes using cosine similarity-based dimensionality reduction methods to maintain a set of effective and complementary base learners within the MAP-Elites framework for evolutionary ensemble learning. Additionally, this paper proposes a reference point synthesis strategy to address the issue of individuals being unevenly distributed in semantic space. The experimental results show that the ensemble model induced by the cosine similarity-based dimensionality reduction method outperforms the models induced by the other seven dimensionality reduction methods in both interactive examples and large-scale experiments. Moreover, reference points are shown to be helpful in improving the algorithm’s effectiveness. The main contribution of this paper is to provide an interactive approach to explore the methods and results, which is detailed in the full paper presented in IEEE Xplore.

中文翻译：

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用于基于遗传编程的集成学习的 MAP-精英：一种交互式方法 [AI-eXplained]


进化集成学习是一个新兴的研究领域，设计合适的质量多样性优化算法以获得一组有效且互补的基学习器非常重要。然而，如何维持这样一组学习者仍然是一个悬而未决的问题。本文提出使用基于余弦相似度的降维方法在 MAP-Elites 进化集成学习框架内维护一组有效且互补的基础学习器。此外，本文提出了一种参考点合成策略来解决个体在语义空间中分布不均匀的问题。实验结果表明，基于余弦相似度的降维方法导出的集成模型在交互式示例和大规模实验中均优于其他七种降维方法导出的模型。此外，参考点被证明有助于提高算法的有效性。本文的主要贡献是提供了一种交互式方法来探索方法和结果，这在 IEEE Xplore 中提出的全文中有详细介绍。