# The Do What's Possible Representation

### Daniel Ashlock, Sierra Gillis, Andrew McEachern, and Jeffrey Tsang

Submitted to CEC 2016

If the complexity of a string is measured by the number of distinct
non-contiguous substrings (those with characters spaced out along the
sequence) it has, then complexity increases the probability that one
of its substrings will solve a given problem. In this study a
collection of representations called Do What's Possible
representations are presented. The representations consist of an
evolvable module that generates complex strings of arbitrary length
together with a generative possibility filter that selects a
non-contiguous substring for its ability to solve one of several test
problems. The filter acts by rejecting loci that encode an impossible
or counterproductive action. Two types of string-generation modules
are compared. Initial experiments verify that the string generators
can be evolved to find complex strings, as characterized by the
entropy of the distribution of fixed-length substrings, and subsequent
experiments demonstrate the system solves a number of different
problems. Parameter studies are performed to tune the
representation. Remarkable performance is achieved on the SAW test
problem and the technique also constructs 8-dimensional Gray codes and
is able to distinguish classes of DNA sequences.