The Do What's Possible Representation

Daniel Ashlock, Sierra Gillis, Andrew McEachern, and Jeffrey Tsang
Submitted to CEC 2016

Abstract PDF eprint

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.