标题： 进化算法中有用多样性的一些信息能通过系统发育度量方法告诉我们吗？ 显示英文标题

标题： What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?

摘要： 人们普遍认为“多样性”与进化算法的成功有关。 然而，多样性是一个广泛的概念，可以以多种方式测量和定义。 到目前为止，大多数进化计算研究使用特定基因型或表型属性的丰富度和/或均匀度来测量多样性。 虽然这些指标是有信息量的，但我们假设其他多样性指标更能预测成功。 系统发育多样性指标是一类在生物学中广泛使用的指标，它们考虑了种群的进化历史。 在这里，我们研究了这两个方面：1）这些指标与传统用于进化计算的指标提供的信息是否不同，以及2）这些指标是否能更好地预测进化计算运行的长期成功。 我们发现，在大多数情况下，系统发育指标与其他多样性指标的行为有明显不同的意义。 此外，我们的结果表明，系统发育多样性确实是成功的更好预测因素。 显示英文摘要

摘要： It is generally accepted that "diversity" is associated with success in evolutionary algorithms. However, diversity is a broad concept that can be measured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly predictive of success. Phylogenetic diversity metrics are a class of metrics popularly used in biology, which take into account the evolutionary history of a population. Here, we investigate the extent to which 1) these metrics provide different information than those traditionally used in evolutionary computation, and 2) these metrics better predict the long-term success of a run of evolutionary computation. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics. Moreover, our results suggest that phylogenetic diversity is indeed a better predictor of success.