University of York, York, UK.
Artif Life. 2011 Fall;17(4):353-64. doi: 10.1162/artl_a_00043. Epub 2011 Jul 15.
We report a study of networks constructed from mutation patterns observed in biology. These networks form evolutionary trajectories, which allow for both frequent substitution of closely related structures, and a small evolutionary distance between any two structures. These two properties define the small-world phenomenon. The mutation behavior between tokens in an evolvable artificial chemistry determines its ability to explore evolutionary space. This concept is underrepresented in previous work on string-based chemistries. We argue that small-world mutation networks will confer better exploration of the evolutionary space than either random or fully regular mutation strategies. We calculate network statistics from two data sets: amino acid substitution matrices, and codon-level single point mutations. The first class are observed data from protein alignments; while the second class is defined by the standard genetic code that is used to translate RNA into amino acids. We report a methodology for creating small-world mutation networks for artificial chemistries with arbitrary node count and connectivity. We argue that ALife systems would benefit from this approach, as it delivers a more viable exploration of evolutionary space.
我们报告了一项对生物学中观察到的突变模式构建的网络的研究。这些网络形成了进化轨迹，允许密切相关的结构频繁替换，并且任何两个结构之间的进化距离很小。这两个特性定义了小世界现象。在可进化的人工化学中，令牌之间的突变行为决定了它探索进化空间的能力。在以前基于字符串的化学研究中，这个概念没有得到充分体现。我们认为，小世界突变网络将比随机或完全规则的突变策略更好地探索进化空间。我们从两个数据集计算网络统计数据：氨基酸替换矩阵和密码子水平的单点突变。第一类是从蛋白质比对中观察到的数据;而第二类是由用于将 RNA 翻译成氨基酸的标准遗传密码定义的。我们报告了一种为具有任意节点计数和连接性的人工化学创建小世界突变网络的方法。我们认为，ALife 系统将受益于这种方法，因为它提供了更可行的进化空间探索。
