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.
YCR Cancer Research Unit, Department of Biology, and YCCSA, Department of Computer Science, Ron Cooke Hub, University of York, Heslington, York YO10 5DD, UK.
YCCSA, Department of Computer Science, Ron Cooke Hub, University of York, Heslington, York YO10 5DD, UK. E-mail: email@example.com