Let’s Talk ’Bout Mutation: Evolutionary Programming for DNA Sequences

Self-driving automata (SDAs) are extensions of finite state automata that both read and output symbols. Previously, genetic algorithms were used to evolve SDAs to generate sequences that closely matched given DNA sequences, with the eventual goal of finding patterns in those sequences that were not achievable using traditional biological methods. Previous work demonstrated that improvements in fitness were almost exclusively due to mutation not crossover. This paper evaluates the use of evolutionary programming (EP) using SDAs as the representation. EP allows for easy handling of multiple types of mutation, including changes in the number of states, which was not available in earlier approaches. This work uses three fitness metrics: primary sequence matching fitness, secondary sequence similarity fitness, and a relative fitness function known as bout score. Tested on a set of six target DNA sequences, the approach matched 84.4–98.2% of each sequence, and discovered some features of the sequences for future exploration.