What Drives Evolution of Self-Driving Automata?

Self-Driving Automata (SDAs) are variations on finite automata that both read and output symbols. They are versatile and practical when used for the generation of data for a variety of problems. In this study, we examine several questions regarding their operation, using sequence matching as a test problem in the analysis. We present a new mutation operator and four dynamic mutation adjusters. We analyze these, along with crossover, for their ability to solve the problem and their relative ability to improve the population; in all of these, we also examine population diversity over time. We find that using mutation that implements a static quantity of changes outperforms one with dynamic changes. Further, while population diversity does decrease somewhat, evolution is still possible.