What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?
What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?
复制标题
关于进化算法中有用的多样性，系统发育指标可以告诉我们什么？
DOI：
10.1007/978-981-16-8113-4_4
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Emily L. Dolson
中科院分区：
文献类型：
--
作者：
Jose Guadalupe Hernandez;Alexander Lalejini;Emily L. Dolson
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，可以用多种方式来衡量和定义。到目前为止，大多数进化计算研究已经使用特定基因型或表型特性的丰富度和/或均匀度来测量多样性。虽然这些指标是翔实的，我们假设，其他多样性指标更强烈地预测成功。系统发育多样性度量是生物学中广泛使用的一类度量，它考虑了种群的进化历史。在这里，我们调查的程度1）这些指标提供不同的信息比那些传统上用于进化计算，2）这些指标更好地预测长期的成功运行的进化计算。我们发现，在大多数情况下，系统发育指标的行为有意义的不同，从其他多样性指标。此外，我们的研究结果表明，系统发育多样性确实是一个更好的预测成功。
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.
解释生命的磁带：基于祖先的分析提供了有关进化动力学的见解和直觉
影响因子：
2.6
作者：
Dolson, Emily;Lalejini, Alexander;Jorgensen, Steven;Ofria, Charles
通讯作者：
Ofria, Charles