@article{ALJmut,
	author = "Alastair Droop and Simon Hickinbotham",
	abstract = "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 behaviour between tokens in an evolvable artificial chemistry defines its ability to explore evolutionary space. This concept is under-represented 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 datasets: amino acid substitution matrices; and codon-level single point mutations. The first class are observed data from protein alignments; whilst 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.",
	title = "Properties of Biological Mutation Networks and Their Implications for ALife.",
	journal = "Artificial Life Journal",
	publisher = "MIT Press",
	volume = "17",
	pages = "353-364",
	year = 2011
}