Population diversity plays an important role in avoiding premature convergence in evolutionary techniques including genetic programming (GP). Obtaining an adequate level of diversity during the evolutionary process has became a concern of many previous researches in GP. This work proposes a new novelty metric for entropy-based diversity measure for GP. The new novelty metric is based on the transformed semantics of models in GP, where the semantics are the set of outputs of a model on the training data and principal component analysis is used for a transformation of the semantics. Based on the new novelty metric, a new diversity preserving framework, which incorporates a new fitness function and a new selection operator, is proposed to help GP achieve a good balance between the exploration and the exploitation, thus enhancing its learning and generalization performance. Compared with two stat-of-the-art diversity preserving methods, the new method can generalize better and reduce the overfitting trend more effectively in most cases. Further examinations on the properties of the search process confirm that the new framework notably enhances the evolvability and locality of GP.

中文翻译：

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基于符号回归的遗传编程中的转换语义保持种群多样性

种群多样性在避免包括遗传编程 (GP) 在内的进化技术中的过早收敛方面起着重要作用。在进化过程中获得足够的多样性水平已成为 GP 先前许多研究的关注点。这项工作提出了一种新的新颖性度量，用于 GP 的基于熵的多样性度量。新的新颖性度量基于 GP 中模型的转换语义，其中语义是模型在训练数据上的输出集，主成分分析用于语义转换。基于新的新颖性度量，提出了一种新的多样性保持框架，该框架结合了新的适应度函数和新的选择算子，以帮助 GP 在探索和开发之间实现良好的平衡，从而提高其学习和泛化性能。与两种最先进的多样性保持方法相比，新方法在大多数情况下可以更好地泛化并更有效地减少过拟合趋势。对搜索过程属性的进一步检查证实，新框架显着增强了 GP 的可进化性和局部性。